Neurobiology Research Review - Illumina | … Neurobiology Research Review INTRODUCTION Neurological disorders are complex diseases caused by a combination of genetic, environmental,

Neurobiology Research ReviewAn Overview of Recent Publications Featuring Illumina Technology

3 An Overview of Publications Featuring Illumina® Technology

CONTENTS

4 IntroductionDiseases

Schizophrenia

Autism Spectrum Disorder

Alzheimer’s Disease

Parkinson’s Disease

23 Genetic MechanismsCopy-Number Variants

Alternative Splicing

Epigenetic Modifications

Small RNAs

Genetic Variants

37 Model Systems Animal Models

43 Stem-Cell Models

45 BiologyImmunity

47 Metagenomics

49 Environmental Factors

50 Single Cells

52 Bibliography

4 Neurobiology Research Review

INTRODUCTION

Neurological disorders are complex diseases caused by a combination of

genetic, environmental, and lifestyle factors. Most neurological diseases—such

as schizophrenia, autism, and Alzheimer’s and Parkinson’s disorders—have been

described many decades ago. However, it is only recently, with the use of next-

generation sequencing (NGS), that their full complexity is being revealed.1,2 There

is an increasing awareness that disease development is driven through a complex

interplay between somatic (non-inherited) mutations, inherited mutations, and

epigenetic modifications.3,4,5,6 This complexity results in extremely weak genotype-

phenotype correlations, and the same disease can present with a variety of

pathological phenotypes in different individuals. Not surprisingly, different neurological

diseases present a spectrum of similar symptomatic profiles (such as dementia, e.g.,

Parkinson’s disease and dementia with Lewy bodies).7 These overlapping symptoms

could indicate the involvement of the same underlying molecular mechanisms. A

diagnosis based on these underlying molecular processes, as well as the observed

phenotype, promises more objective and accurate diagnosis and treatment in

the future.8

The increased information obtained from NGS, along with a variety of library

preparation methods, offers an impressive armamentarium of tools to unravel the

genomic and epigenomic aspects of psychiatric and neurodegenerative diseases.

The improved understanding also could translate ultimately into the development of

new, effective therapies. Additionally, whole-genome sequencing (WGS) potentially

could detect predisposition to these disorders, to allow preventative care and

early intervention. A recently announced massive 100,000 Genomes project,

initiated by Genomics England, signifies the importance of genomic diagnostics for

tomorrow’s medicine.

Diagnostic yield for patients with severe intellectual disability (IQ < 50), specified by technology: genomic microarrays, whole-exome sequencing (WES), and WGS. Percentages indicate the number of patients in whom a conclusive cause was identified using the specified technique.9

1. Bras J., Guerreiro R. and Hardy J. (2012) Use of next-generation sequencing and other whole-genome strategies to dissect neurologi-cal disease. Nat Rev Neurosci 13: 453-464

2. Koboldt D. C., Steinberg K. M., Larson D. E., Wilson R. K. and Mardis E. R. (2013) The next-generation sequencing revolution and its impact on genomics. Cell 155: 27-38

3. Yu T. W., Chahrour M. H., Coulter M. E., Jiralerspong S., Okamura-Ikeda K., et al. (2013) Using whole-exome sequencing to identify inherited causes of autism. Neuron 77: 259-273

4. Fromer M., Pocklington A. J., Kavanagh D. H., Williams H. J., Dwyer S., et al. (2014) De novo mutations in schizophrenia implicate synaptic networks. Nature 506: 179-184

5. De Jager P. L., Srivastava G., Lunnon K., Burgess J., Schalkwyk L. C., et al. (2014) Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 17: 1156-1163

6. Chung S. J., Armasu S. M., Anderson K. J., Biernacka J. M., Lesnick T. G., et al. (2013) Genetic susceptibility loci, environmental expo-sures, and Parkinson's disease: a case-con-trol study of gene-environment interactions. Parkinsonism Relat Disord 19: 595-599

7. McCarthy S. E., Gillis J., Kramer M., Lihm J., Yoon S., et al. (2014) De novo mutations in schizophrenia implicate chromatin remodeling and support a genetic overlap with autism and intellectual disability. Mol Psychiatry 19: 652-658


9. Gilissen C., Hehir-Kwa J. Y., Thung D. T., van de Vorst M., van Bon B. W., et al. (2014) Genome sequencing identifies major causes of severe intellectual disability. Nature 511: 344-347


Diseases

Neurological diseases represent a daunting spectrum of complex multifactorial

pathologies, ranging from subtle to life-threatening. To illustrate the most recent

research and the use of genomics, this review focuses on schizophrenia and

autism as examples of complex neurodevelopmental disorders, and Alzheimer’s

and Parkinson’s as examples of neurodegenerative diseases. The approaches

and techniques used in these studies can be applied to a wide variety of

neurological diseases.

Schizophrenia

Schizophrenia is one of the most complex psychiatric diseases, and it affects as

much as 1% of the global adult population.10 The most common symptoms include

irrational thinking, auditory hallucinations, false beliefs, and reduced social activity.

The disease typically develops between 12 and 25 years of age and is a highly

heritable, polygenic disorder. Recent genomic analysis studies have shown that

schizophrenia is attributed to over a thousand gene loci, many of which appear

in non-coding parts of the genome.11-15 A significant subset of risk alleles for

schizophrenia is also implicated across other diseases in this diagnostic category,

such as bipolar disorder, autism, and depression.16,17,18 All these diseases are truly

spectrum disorders, which has complicated understanding of their genetic causes

and the development of targeted therapies. However, the advent of high-throughput

gene sequencing technology provides a tool for deeper analysis of the genetic basis

of these diseases, and it holds promise for unraveling the complex interplay between

multiple genetic and epigenetic modifications.

Genotype-phenotype correlations in schizophrenia are extremely weak, and the same

disease can present with a variety of pathological phenotypes in different patients.

Studies with twins revealed that over 80% of the risk of developing schizophrenia

comes from genetic predisposition, but exposure to environmental risk factors can

play a significant role.20 The first genetic mutations detected in schizophrenia were

rare variants, including copy-number variants (CNVs).21 Altogether, these variants

account for almost 20% of disease cases.22 The remaining genetic hits are most likely

represented by common variants.23 Presumably, the effects of individual common

variants are mild; however, in combination, they may be sufficient to trigger the onset

of schizophrenia.24,25

“Schizophrenia liability is being mapped to hundreds, perhaps ultimately more than a thousand, genetic loci, each contributing a small increment of risk.” Hyman 2014

10. Elert E. (2014) Aetiology: Searching for schizo-phrenia's roots. Nature 508: S2-3

11. Ripke S., O'Dushlaine C., Chambert K., Moran J. L., Kahler A. K., et al. (2013) Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat Genet 45: 1150-1159

12. Purcell S. M., Moran J. L., Fromer M., Ruderfer D., Solovieff N., et al. (2014) A polygenic bur-den of rare disruptive mutations in schizophre-nia. Nature 506: 185-190

13. Hyman S. E. (2014) Perspective: Revealing molecular secrets. Nature 508: S20


15. McCarroll S. A. and Hyman S. E. (2013) Progress in the genetics of polygenic brain disorders: significant new challenges for neu-robiology. Neuron 80: 578-587

16. Kirov G., Rees E., Walters J. T., Escott-Price V., Georgieva L., et al. (2014) The penetrance of copy number variations for schizophrenia and developmental delay. Biol Psychiatry 75: 378-385

17. Cross-Disorder Group of the Psychiatric Ge-nomics C., Lee S. H., Ripke S., Neale B. M., Faraone S. V., et al. (2013) Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet 45: 984-994

18. Cukier H. N., Dueker N. D., Slifer S. H., Lee J. M., Whitehead P. L., et al. (2014) Exome sequencing of extended families with autism reveals genes shared across neurodevelop-mental and neuropsychiatric disorders. Mol Autism 5: 1

19. Wright J. (2014) Genetics: Unravelling com-plexity. Nature 508: S6-7


21. Wright J. (2014) Genetics: Unravelling complexity. Nature 508: S6-7




25. Gaugler T., Klei L., Sanders S. J., Bodea C. A., Goldberg A. P., et al. (2014) Most genetic risk for autism resides with common variation. Nat Genet 46: 881-885


Along with determining the individual mutations leading to schizophrenia, the power

of genetic analysis is expected to give answers to some other important questions,

such as why the development of schizophrenia is associated with accelerated

ageing26 or why schizophrenic patients suffer from heart, lung, and metabolic

diseases at young ages and at a much higher rate than the general population.29

These effects could be due to additional lifestyle risks, such as substance abuse and

smoking. Approximately 50% of patients with chronic schizophrenia have substance-

use disorder, and their risk to develop this disorder is 4.6 times higher than for the

general population. Over 80% of schizophrenic patients in the U.S. are also heavy

smokers,30 as nicotine serves as an agonist of the nicotine acetylcholine receptor and

presumably can attenuate some cognitive impairment associated

with schizophrenia.31

Smoking and substance abuse are common among schizophrenia patients. Over 80% of schizophrenic patients in the U.S. are heavy smokers,32 as nicotine presumably can attenuate some of the cognitive impairment associated with schizophrenia.

Early, accurate, and objective diagnosis of the underlying molecular mechanisms

of the disease will allow a better control of the disease symptoms with available

therapies. In the more distant future, an improved understanding of the disease

should lead to the development of more effective, targeted, and

personalized therapies.

ReviewsAnthes E. (2014) Ageing: Live faster, die younger. Nature 508: S16-17

Brody H. (2014) Schizophrenia. Nature 508: S1

Dolgin E. (2014) Therapeutics: Negative feedback. Nature 508: S10-11

Elert E. (2014) Aetiology: Searching for schizophrenia's roots. Nature 508: S2-3

Horvath S. and Mirnics K. (2014) Immune system disturbances in schizophrenia. Biol Psychiatry 75: 316-323

Hyman S. E. (2014) Perspective: Revealing molecular secrets. Nature 508: S20

Singh S., Kumar A., Agarwal S., Phadke S. R. and Jaiswal Y. (2014) Genetic insight of schizophrenia: past and future perspectives. Gene 535: 97-100

Schizophrenia Working Group of the Psychiatric Genomics C. (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511: 421-427

Wright J. (2014) Genetics: Unravelling complexity. Nature 508: S6-7

Malhotra D. and Sebat J. (2013) CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell 148: 1223-1241

Schreiber M., Dorschner M. and Tsuang D. (2013) Next-generation sequencing in schizophrenia and other neuropsychiatric disorders. Am J Med Genet B Neuropsychiatr Genet 162B: 671-678

26. Brody H. (2014) Schizophrenia. Nature 508: S1

27. Anthes E. (2014) Ageing: Live faster, die younger. Nature 508: S16-17


29. Dixon L. (1999) Dual diagnosis of substance abuse in schizophrenia: prevalence and impact on outcomes. Schizophr Res 35 Suppl: S93-100

30. de Leon J. and Diaz F. J. (2005) A meta-anal-ysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors. Schizophr Res 76: 135-157

31. Ng E., McGirr A., Wong A. H. and Roder J. C. (2013) Using rodents to model schizophrenia and substance use comorbidity. Neurosci Biobehav Rev 37: 896-910

32. de Leon J. and Diaz F. J. (2005) A meta-anal-ysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors. Schizophr Res 76: 135-157


ReferencesFromer M., Pocklington A. J., Kavanagh D. H., Williams H. J., Dwyer S., et al. (2014) De novo mutations in schizophrenia implicate synaptic networks. Nature 506: 179-184Of the known risk alleles for schizophrenia, the only ones definitively shown to confer considerable increments in risk are rare chromosomal CNVs that involve deletion or duplication of thousands of bases of DNA. This study examined the effect of small de novo mutations affecting one or a few nucleotides. By Illumina HiSeq WES of 623 schizophrenia trios, the authors assessed de novo mutation rates and shared genetic etiology for schizophrenia, intellectual disability, and autism-spectrum disorders (ASDs). They found several insights to suggest a common etiological mechanism.

Illumina Technology: HiSeq for exome sequencing

Karayannis T., Au E., Patel J. C., Kruglikov I., Markx S., et al. (2014) Cntnap4 differentially contributes to GABAergic and dopaminergic synaptic transmission. Nature 511: 236-240In an effort to understand the pathological development of neurological disorders, genetic effects have been studied in the context of proteins that are expressed in neural cells. In this study, the authors characterized the effect of CNTNAP4 knockouts on mouse behavior and development, and relate these results to the findings of CNVs in humans across a region including the CNTNAP2 gene. The authors found that CNTNAP4 is localized presynaptically, and its loss leads to a reduction in the output of cortical parvalbumin (PV)-positive GABAergic basket cells. In addition, CNTNAP4-mutant mice showed defects in these neuronal populations and exhibited sensory-motor gating and grooming endophenotypes.

Illumina Technology: HumanHap550, HumanOmni1-Quad

Purcell S. M., Moran J. L., Fromer M., Ruderfer D., Solovieff N., et al. (2014) A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506: 185-190Identifying gene associations for complex genetic diseases remains challenging, with small sample sizes being a hindrance for finding significant effects. In this study of schizophrenia, the authors performed WES, based on Illumina technology, of 2,536 schizophrenia cases and 2,543 controls. They identified disruptive mutations distributed across many genes; however, no individual gene-based test for low frequency and moderately large effect achieves significance after correction for multiple testing.

Illumina Technology: HiSeq 2000, Genome AnalyzerIIx

Schizophrenia Working Group of the Psychiatric Genomics C. (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511: 421-427Schizophrenia is a highly heritable disorder, but the heritability is not found in a single gene effect. In this largest genome-wide association study (GWAS) for schizophrenia to date, the authors used single-nucleotide polymorphism (SNP) arrays for 36,989 cases and 113,075 controls to determine genetic risk factors for the disorder. The authors found that the significant genetic associations were not randomly spread across the genome, but enriched among genes expressed in brain and genes that have been associated with typical co-morbidity diagnoses, such as ASD and intellectual disability. Interestingly, links were also enriched within genes related to immunity, which fits the existing hypothesis of immune dysregulation in schizophrenia.

Illumina Technology: Human1M, HumanOmni2.5, HumanOmniExpress, HumanHap550, Human610, HumanHap650Y, HumanHap300

Stefansson H., Meyer-Lindenberg A., Steinberg S., Magnusdottir B., Morgen K., et al. (2014) CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 505: 361-366Certain CNVs contribute to the pathogenesis of schizophrenia and autism. In this study, the authors investigated the influence of these CNVs on phenotypes separate from those of the mentioned diseases. In a big population-wide study of nearly a third of the Icelandic population (n = 101,655), the authors used Illumina SNP microarrays to test for associations of CNVs with cognitive deficits, dyslexia, dyscalculia, and brain structure changes. The authors found that the 15q11.2(BP1-BP2) deletion affects brain structure in a pattern consistent with first-episode psychosis in both schizophrenia and dyslexia.

Illumina Technology: HumanHap300, HumanCNV370-Duo, HumanHap650Y, Human1M, HumanOmni2.5, HumanOmniExpress, HumanOmni1S


Yoon K. J., Nguyen H. N., Ursini G., Zhang F., Kim N. S., et al. (2014) Modeling a genetic risk for schizophrenia in iPSCs and mice reveals neural stem cell deficits associated with adherens junctions and polarity. Cell Stem Cell 15: 79-91Defects in brain development can contribute to the onset of neuropsychiatric disorders. This study set out to identify the functional role of the 15q11.2 deletion on neural development using induced pluripotent stem cell (iPSC)-derived human neural precursor cells (hNPCs) by RNA-Seq and SNP-genotyping arrays. They found that haploinsufficiency of CYFIP1, a gene within 15q11.2, affects radial glial cells, leading to their ectopic localization outside of the ventricular zone.

Illumina Technology: HumanOmni2.5S

Stoll G., Pietilainen O. P., Linder B., Suvisaari J., Brosi C., et al. (2013) Deletion of TOP3beta, a component of FMRP-containing mRNPs, contributes to neurodevelopmental disorders. Nat Neurosci 16: 1228-1237Genetic studies, including studies of mRNA-binding proteins, have shed new light on the connection of mRNA metabolism to disease. In this study, the authors found that deletion of the TOP3b gene was associated with neurodevelopmental disorders in the Northern Finnish population. Combining the genotyping with photoactivatable ribonucleoside–enhanced crosslinking and immunoprecipitation (PAR-CLIP), the authors found that the recruitment of TOP3b to cytosolic messenger ribonucleoproteins (mRNPs) was coupled to the co-recruitment of FMRP, the disease gene involved in Fragile X syndrome.

Illumina Technology: Human Gene Expression, Human610-Quad, HumanHap300, HumanCNV370-Duo

RNase T1 digestion

UV 365 nm

cDNA

Proteinase K

RNA-protein complex

RNA extraction and reverse transcription

Incorporate 4-thiouridine (4SU) into transcripts of cultured cells

Photoactivatable ribonucleoside–enhanced crosslinking and immunoprecipitation (PAR-CLIP) maps RNA-binding proteins (RBPs).33 This approach is similar to high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP) and cross-linking immunoprecipitation sequencing (CLIP-Seq), but uses much more efficient crosslinking to stabilize the protein-RNA complexes. The requirement for a photoactivatable ribonucleoside limits this approach to cell culture and in vitro systems. In this method, 4-thiouridine (4-SU) and 6-thioguanosine (6-SG) are incorporated into transcripts of cultured cells. Ultraviolet irradiation crosslinks 4-SU/6-SG–labeled transcripts to interacting RBPs. The targeted complexes are immunoprecipitated and digested with RNase T1, followed by Proteinase K, before RNA extraction. The RNA is reverse-transcribed to cDNA and sequenced. Deep sequencing of cDNA accurately maps RBPs interacting with labeled transcripts. (For more methods, see: http://applications.illumina.com/applications/sequencing/ngs-library-prep/library-prep-methods.ilmn)

Ionita-Laza I., Xu B., Makarov V., Buxbaum J. D., Roos J. L., et al. (2014) Scan statistic-based analysis of exome sequencing data identifies FAN1 at 15q13.3 as a susceptibility gene for schizophrenia and autism. Proc Natl Acad Sci U S A 111: 343-348

McCarthy S. E., Gillis J., Kramer M., Lihm J., Yoon S., et al. (2014) De novo mutations in schizophrenia implicate chromatin remodeling and support a genetic overlap with autism and intellectual disability. Mol Psychiatry 19: 652-658

Todarello G., Feng N., Kolachana B. S., Li C., Vakkalanka R., et al. (2014) Incomplete penetrance of NRXN1 deletions in families with schizophrenia. Schizophr Res 155: 1-7

Ripke S., O'Dushlaine C., Chambert K., Moran J. L., Kahler A. K., et al. (2013) Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat Genet 45: 1150-1159

33. Hafner M., Landgraf P., Ludwig J., Rice A., Ojo T., et al. (2008) Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods 44: 3-12


Autism Spectrum Disorder

Autism spectrum disorder (ASD) comprises a group of polygenic, multi-locus

disorders,34 often accompanied by symptoms of other disorders, such as

developmental disability/intellectual disability (DD/ID; over 40% of ASD cases),

attention deficit/hyperactivity disorder (ADHD; 59%–75%), obsessive-compulsive

disorder (OCD; 60%), epilepsy (7%–46%), and other neurological and behavioral

patterns.35,36 Autism rates have been increasing rapidly, from 0.7% of the population

in 2000 to 1.1% in early 2010.37 In the U.S., 1 in 68 children has been diagnosed

as autistic. This trend can be partially attributed to the improved diagnosis of the

disease. Improved understanding of the genetic causes of ASD is anticipated to

facilitate development of palliative or therapeutic care for affected individuals. It is

also expected to be instrumental in providing a more accurate method to assess the

mental condition of at-risk populations, such as criminals and individuals with other

psychiatric pathologies.38,39,40

Autism is primarily a genetic disease, with 15–30 times increased risk of disease

development in siblings of autistic children.41 Heritability of this disease has

been estimated as high as 90%–96%, suggesting yet unidentified non-genetic

causes.42,43,44 A more accurate, systematic approach is still needed to improve

the distinction between essential autism and complex (syndromic, sporadic)

autism (Table 1). Genomic approaches promise to be efficient and reliable tools for

distinguishing between various types of autism.45

Table 1: Types of autism

Type of DiseasePercentage

of CasesDisease Characteristics

Essential autism46w 75% Higher male to female ratioLack of dysmorphic featuresHigher sibling recurrence riskPositive family historyCommon gene variants

Complex (syndromic, sporadic) autism47

25% Large number of highly penetrant rare mutations

Similar to schizophrenia, ASD is a heterogenic disorder.48 This heterogeneity may be

observed not only across individuals, but even across different sections of brain.

Gene expression may change based on the timing of the analysis, as shown in

experiments with mice.49

“Two years from now, researchers will need a larger T-shirt to flaunt their findings.” Wright 2014

34. Losh M., Childress D., Lam K. and Piven J. (2008) Defining key features of the broad autism phenotype: a comparison across parents of multiple- and single-incidence autism families. Am J Med Genet B Neuropsychiatr Genet 147B: 424-433

35. Buxbaum J. (2013) The Neuroscience of Autism Spectrum Disorders.

36. Lee H., Lin M. C., Kornblum H. I., Papazian D. M. and Nelson S. F. (2014) Exome sequencing identifies de novo gain of function missense mutation in KCND2 in identical twins with autism and seizures that slows potassium channel inactivation. Hum Mol Genet 23: 3481-3489

37. Gilbert J. A., Krajmalnik-Brown R., Porazinska D. L., Weiss S. J. and Knight R. (2013) Toward effective probiotics for autism and other neuro-developmental disorders. Cell 155: 1446-1448

38. King C. and Murphy G. H. (2014) A Systematic Review of People with Autism Spectrum Disor-der and the Criminal Justice System. J Autism Dev Disord

39. Gadow K. D. (2013) Association of schizophre-nia spectrum and autism spectrum disorder (ASD) symptoms in children with ASD and clinic controls. Res Dev Disabil 34: 1289-1299

40. Smith K. R. and Matson J. L. (2010) Social skills: differences among adults with intellectual disabil-ities, co-morbid autism spectrum disorders and epilepsy. Res Dev Disabil 31: 1366-1372

41. Szatmari P. (1999) Heterogeneity and the genet-ics of autism. J Psychiatry Neurosci 24: 159-165

42. Rosti R. O., Sadek A. A., Vaux K. K. and Gleeson J. G. (2014) The genetic landscape of autism spectrum disorders. Dev Med Child Neurol 56: 12-18


44. Nava C., Keren B., Mignot C., Rastetter A., Chantot-Bastaraud S., et al. (2014) Prospective diagnostic analysis of copy number variants using SNP microarrays in individuals with autism spectrum disorders. Eur J Hum Genet 22: 71-78



47. Ghosh A., Michalon A., Lindemann L., Fontoura P. and Santarelli L. (2013) Drug discovery for autism spectrum disorder: challenges and op-portunities. Nat Rev Drug Discov 12: 777-790

48. Willsey A. J., Sanders S. J., Li M., Dong S., Tebbenkamp A. T., et al. (2013) Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155: 997-1007



CNVs were the first type of mutation associated with autism.50,51 The de novo rate of

CNVs is three to seven times higher than in controls. The gene groups most affected

by autism-associated CNVs are GTPase/Ras, ubiquitin degradation genes, and

genes involved in synapse development, axon targeting, and neuron motility.52,53,54

Large CNVs are present in 5%–10% of ASD patients, primarily in those with a

syndromic ASD phenotype.55,56,57 Private CNVs in autistic individuals often affect

genes encoding synaptic proteins and neuronal cell-adhesion proteins.58

(see Copy-Number Variants)

Furthermore, ASD can also be caused by rare mutations, deletions, duplications,59

and larger chromosomal abnormalities, which may be inherited or arise de novo.60

Monogenic mutations known to date contribute to 2%–5% of syndromic cases, with

Fragile X chromosome, PTEN macrocephaly, and tuberous sclerosis being the most

common abnormalities.61,62 PTEN mutations are also strongly associated with

tumor syndromes.63

Twin studies have become a standard model in the research of psychiatric diseases. They allow assessment of the contribution of genes and the environment to disease risk.

Recent WES and WGS studies have identified multiple, high-confidence ASD

genes.64 Of two large-scale WGS projects, one was initiated by the U.K. government

in collaboration with Illumina and the Wellcome Trust (100,000 Genomes project),

and the second was initiated by Beijing Genomics Institute (BGI) in collaboration with

Autism Speaks (Autism Genome 10K project). The pilot results of the latter study

have been published by Jiang et al.65

50. Iafrate A. J., Feuk L., Rivera M. N., Listewnik M. L., Donahoe P. K., et al. (2004) Detection of large-scale variation in the human genome. Nat Genet 36: 949-951

51. Redon R., Ishikawa S., Fitch K. R., Feuk L., Perry G. H., et al. (2006) Global variation in copy number in the human genome. Nature 444: 444-454

52. Glessner J. T., Wang K., Cai G., Korvatska O., Kim C. E., et al. (2009) Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459: 569-573

53. Gilman S. R., Iossifov I., Levy D., Ronemus M., Wigler M., et al. (2011) Rare de novo variants as-sociated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70: 898-907

54. Sakai Y., Shaw C. A., Dawson B. C., Dugas D. V., Al-Mohtaseb Z., et al. (2011) Protein interac-tome reveals converging molecular pathways among autism disorders. Sci Transl Med 3: 86ra49

55. Sanders S. J., Ercan-Sencicek A. G., Hus V., Luo R., Murtha M. T., et al. (2011) Multiple recur-rent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70: 863-885

56. Levy D., Ronemus M., Yamrom B., Lee Y. H., Leotta A., et al. (2011) Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70: 886-897

57. Guilmatre A., Dubourg C., Mosca A. L., Legallic S., Goldenberg A., et al. (2009) Recurrent rearrangements in synaptic and neurodevelop-mental genes and shared biologic pathways in schizophrenia, autism, and mental retardation. Arch Gen Psychiatry 66: 947-956

58. Nava C., Keren B., Mignot C., Rastetter A., Chantot-Bastaraud S., et al. (2014) Prospective diagnostic analysis of copy number variants using SNP microarrays in individuals with autism spectrum disorders. Eur J Hum Genet 22: 71-78.

59. Banerjee S., Riordan M. and Bhat M. A. (2014) Genetic aspects of autism spectrum disorders: insights from animal models. Front Cell Neurosci 8: 58


61. Miles J. H. (2011) Autism spectrum disorders--a genetics review. Genet Med 13: 278-294

62. Schaaf C. P. and Zoghbi H. Y. (2011) Solving the autism puzzle a few pieces at a time. Neuron 70: 806-808



65. Jiang Y. H., Yuen R. K., Jin X., Wang M., Chen N., et al. (2013) Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. Am J Hum Genet 93: 249-263


EpigeneticsSusceptibility to ASD can arise at both the genetic and epigenetic levels.66 Several

groups have independently identified multiple differentially methylated regions (DMRs)

in post-mortem samples of autistic individuals. These biologically diverse gene

regions include DNase hypersensitive sites and an alternative transcript termination

site.67,68,69 These studies provide an additional level of evidence for the role of

epigenetics in complex diseases, such as ASD.

ReviewsBanerjee S., Riordan M. and Bhat M. A. (2014) Genetic aspects of autism spectrum disorders: insights from animal models. Front Cell Neurosci 8: 58

Rosti R. O., Sadek A. A., Vaux K. K. and Gleeson J. G. (2014) The genetic landscape of autism spectrum disorders. Dev Med Child Neurol 56: 12-18

Buxbaum J. (2013) The Neuroscience of Autism Spectrum Disorders.

Ebert D. H. and Greenberg M. E. (2013) Activity-dependent neuronal signalling and autism spectrum disorder. Nature 493: 327-337

Gadad B. S., Hewitson L., Young K. A. and German D. C. (2013) Neuropathology and animal models of autism: genetic and environmental factors. Autism Res Treat 2013: 731935

Ghosh A., Michalon A., Lindemann L., Fontoura P. and Santarelli L. (2013) Drug discovery for autism spectrum disorder: challenges and opportunities. Nat Rev Drug Discov 12: 777-790

Persico A. M. and Napolioni V. (2013) Autism genetics. Behav Brain Res 251: 95-112

Wohr M. and Scattoni M. L. (2013) Behavioural methods used in rodent models of autism spectrum disorders: current standards and new developments. Behav Brain Res 251: 5-17


ReferencesGaugler T., Klei L., Sanders S. J., Bodea C. A., Goldberg A. P., et al. (2014) Most genetic risk for autism resides with common variation. Nat Genet 46: 881-885Although ASDs have been studied widely, the proportion and nature of genetic heritability is uncertain. This study analyzed the largest autism cohort data to date, including 1.6 million Swedish families with at least two children and ~5,700 individuals with strict autism diagnoses. Using Illumina SNP arrays, the authors investigated the contribution of rare versus common genetic variants to the disease. They conclude that the heritability is ~52.4%, with common variation as the biggest contributor. Rare, de novo mutations contribute substantially to individual liability, yet their contribution to variance in liability is modest at 2.6%.

Illumina Technology: OmniExpress and Exome

Lee H., Lin M. C., Kornblum H. I., Papazian D. M. and Nelson S. F. (2014) Exome sequencing identifies de novo gain of function missense mutation in KCND2 in identical twins with autism and seizures that slows potassium channel inactivation. Hum Mol Genet 23: 3481-3489Numerous studies have reported comorbidity of autism and epilepsy, but the relationship between the two disorders is unknown. In this study, identical twins, affected by both autism and severe intractable seizures, were studied using exome sequencing. A novel variant in the KCND2 gene was observed in both twins. The de novo mutation is located in the protein coding the Kv4.2 potassium channel, and the authors expressed the mutant protein in Xenopus oocytes to observe functional effects. Expression analysis showed that the mutation dominantly impairs the closed-state inactivation of the potassium channel, strongly supporting KCND2 as the causal gene for epilepsy in this family.

Illumina Technology: HiSeq 2000, Illumina Paired-End Sequencing Library Prep Protocol

66. Wong C. C., Meaburn E. L., Ronald A., Price T. S., Jeffries A. R., et al. (2014) Methylomic analysis of monozygotic twins discordant for autism spectrum disorder and related be-havioural traits. Mol Psychiatry 19: 495-503

67. Ladd-Acosta C., Hansen K. D., Briem E., Fallin M. D., Kaufmann W. E., et al. (2013) Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry




King I. F., Yandava C. N., Mabb A. M., Hsiao J. S., Huang H. S., et al. (2013) Topoisomerases facilitate transcription of long genes linked to autism. Nature 501: 58-62Topoisomerases are expressed throughout the developing and adult brain, and are mutated in some individuals with ASD. However, the mechanism by which topoisomerases impact ASD is unknown. By transcriptome sequencing, in combination with genome-wide mapping of RNA polymerase II density in neurons, the authors found that expression of long genes was reduced after knockdown of topoisomerase in neurons. The authors noted that many high-confidence ASD candidate genes are exceptionally long and were reduced in expression after TOP1 inhibition. This observation suggests that defective topoisomerases could contribute to ASD.

Illumina Technology: HiSeq 2000, TruSeq RNA, TruSeq for ChIP-Seq

Ladd-Acosta C., Hansen K. D., Briem E., Fallin M. D., Kaufmann W. E., et al. (2013) Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry Genetic as well as environmental factors are responsible for ASDs. In this study, the authors measured over 485,000 CpG loci and identified 4 genome-wide significantly different DMRs from 19 autism cases in human brain tissue. This study highlights a new selected set of affected genes.

Illumina Technology: HumanMethylation450

Willsey A. J., Sanders S. J., Li M., Dong S., Tebbenkamp A. T., et al. (2013) Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155: 997-1007Recent studies employing WES and WGS have identified nine high-confidence ASD genes. This study examined the contribution of these nine genes to the common phenotype by combining Illumina WES and RNA-Seq data into co-expression networks. The authors explain how these networks will guide future ASD research by indicating which genes are most likely to have overlapping molecular, cellular or circuit-level phenotypes.

Illumina Technology: HiSeq 2000, Genome Analyzer

Yu T. W., Chahrour M. H., Coulter M. E., Jiralerspong S., Okamura-Ikeda K., et al. (2013) Using whole-exome sequencing to identify inherited causes of autism. Neuron 77: 259-273Steel syndrome is a developmental structural disorder first described in 1993 in 23 Hispanic children from Puerto Rico. This paper presents the genomic analysis of a family with two affected siblings. The authors used whole-exome sequencing using the Baylor College of Medicine Human Genome Sequencing Center (BCM-HGSC) core design followed by sequencing of both affected siblings, parents, and an affected cousin and her unaffected parents. By filtering the detected genetic variants by segregation, the authors discovered a single homozygous missense variant segregating with the disorder. The variant disrupts the collagen gene COL27A1, which codes for a protein expressed in developing cartilage.

Illumina Technology: HiSeq 2000, HumanOmniExpress

Lionel A. C., Tammimies K., Vaags A. K., Rosenfeld J. A., Ahn J. W., et al. (2014) Disruption of the ASTN2/TRIM32 locus at 9q33.1 is a risk factor in males for autism spectrum disorders, ADHD and other neurodevelopmental phenotypes. Hum Mol Genet 23: 2752-2768


Nava C., Keren B., Mignot C., Rastetter A., Chantot-Bastaraud S., et al. (2014) Prospective diagnostic analysis of copy number variants using SNP microarrays in individuals with autism spectrum disorders. Eur J Hum Genet 22: 71-78

Wong C. C., Meaburn E. L., Ronald A., Price T. S., Jeffries A. R., et al. (2014) Methylomic analysis of monozygotic twins discordant for autism spectrum disorder and related behavioural traits. Mol Psychiatry 19: 495-503


Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia, affecting over 40

million people world-wide, and the incidence may double by the year 2050.70 Eleven

percent of people 65 or older and 32% of people 85 or older are affected by this fatal

neurodegenerative disorder.71 Early symptoms of AD include gradually worsening

ability to memorize new information,72 followed by confusion, irritability, trouble with

language, and long-term memory loss. The disease destroys memory and cognitive

skills through the accumulation of two types of abnormal insoluble aggregates in the

brain: extracellular ß-amyloid plaques and intracellular neurofibrillary tangles. The

aggregates disrupt the intricate interplay between brain neurons, which eventually

cause the death of the neurons with consequent significant shrinkage of the

brain volume.73

Pathogenic ß-amyloid protein belongs to a group of prion proteins, which kick-start

a chain reaction of destructive processes by engaging new ß-amyloid “seeds” into

formation of insoluble oligomers and spreading across the brain. However, unlike

“mad cow disease” (bovine spongiform encephalopathy or BSE) prions, AD-related

amyloidosis is not infectious and cannot be transmitted between individuals.74

Neurofibrillary tangles are formed intracellularly by oligomerization of the hyper-

phosphorylated form of Tau protein, which is normally abundant in axons and is

responsible for maintaining the structure of microtubules. During the disease, tau

protein is misfolded and mislocalized to the neuronal soma.75

Beta-amyloid protein is the major component of amyloid plaques.

Alzheimer’s disease can be present in one of two forms: early-onset AD (EOAD,

30–60 years old) and late-onset AD (LOAD) (Table 2). EOAD is mostly a genetic

disease, whereas LOAD is a sporadic disease, associated with a complex interplay

among different mutations.

70. Brookmeyer R., Johnson E., Ziegler-Graham K. and Arrighi H. M. (2007) Forecasting the global burden of Alzheimer's disease. Alzhei-mers Dement 3: 186-191

71. Lu H., Liu X., Deng Y. and Qing H. (2013) DNA methylation, a hand behind neurodegenerative diseases. Front Aging Neurosci 5: 85


73. Huang Y. and Mucke L. (2012) Alzheimer mechanisms and therapeutic strategies. Cell 148: 1204-1222

74. Aguzzi A. and Rajendran L. (2009) The trans-cellular spread of cytosolic amyloids, prions, and prionoids. Neuron 64: 783-790



Table 2: Types of AD

Type of DiseaseAge of Onset

Percentage of Cases

Disease Characteristics

Early-onset AD (EOAD)

30–60 years 2%–5% Mostly genetic76

Late-onset AD (LOAD)

>60 years 95%–98%77 Strong role for epigenetic markers, such as DNA methylation78

EOAD is associated with mutations in three genes: amyloid precursor protein

(APP, integral Type I membrane glycoprotein) and presenelins PSEN1 and PSEN2.

However, these mutations reportedly account for fewer than 2% of all AD cases.79,80

Mutations in these genes deregulate the APP pathway and cause accumulation of

plaques built of ß-amyloid protein.81 Interestingly, some mutations in APP can be

protective against AD,82,83 highlighting the importance of deep genetic analysis and

correlative studies of this disease.

LOAD had, until recently, only one known genetic risk factor: the E4 variant of the

apolipoprotein E gene (APOE).84 The risk of developing LOAD in individuals with

two copies of APOE4 (2% of population) is as high as 60% at 85 years old, and

in individuals with one copy of this gene (25% of the population), it is 30%.85 Each

additional copy of APOE4 increases the risk of developing AD by a factor of three

or more.86 It is possible that low-frequency variants with large influence on LOAD

pathogeneses may have been missed by traditional GWAS. Sequence-based

association studies may be able to identify risk alleles in complex diseases, and it is

anticipated that these studies will elucidate low-frequency variants with large

effect sizes.87

Extracellular amyloid plaques stick to each other, as well as to the neuron. They disrupt neuronal networks, causing death of neurons and impairment of brain activity.

An interesting experimental approach proposes that WES be run on a thoroughly

selected subgroup of individuals at increased risk of AD, and this analysis can be

followed by a combination of genotyping and resequencing assays.88

76. Guerreiro R. J., Gustafson D. R. and Hardy J. (2012) The genetic architecture of Alzheimer's disease: beyond APP, PSENs and APOE. Neurobiol Aging 33: 437-456

77. Coppede F. (2014) The potential of epigenetic therapies in neurodegenerative diseases. Front Genet 5: 220

78. Mitsui J. and Tsuji S. (2014) Genomic Aspects of Sporadic Neurodegenerative Diseases. Biochem Biophys Res Commun 452: 221-225


80. Guerreiro R. J., Gustafson D. R. and Hardy J. (2012) The genetic architecture of Alzheimer's disease: beyond APP, PSENs and APOE. Neurobiol Aging 33: 437-456


82. Jonsson T., Atwal J. K., Steinberg S., Snaedal J., Jonsson P. V., et al. (2012) A mutation in APP protects against Alzheimer's disease and age-related cognitive decline. Nature 488: 96-99

83. De Strooper B. and Voet T. (2012) Alzheimer's disease: A protective mutation. Nature 488: 38-39


85. Genin E., Hannequin D., Wallon D., Sleegers K., Hiltunen M., et al. (2011) APOE and Alzhei-mer disease: a major gene with semi-dominant inheritance. Mol Psychiatry 16: 903-907

86. Medland S. E., Jahanshad N., Neale B. M. and Thompson P. M. (2014) Whole-genome analyses of whole-brain data: working within an expanded search space. Nat Neurosci 17: 791-800


88. Cruchaga C., Karch C. M., Jin S. C., Benitez B. A., Cai Y., et al. (2014) Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer's disease. Nature 505: 550-554


Aging and AD are associated with a spectrum of epigenetic changes, such as

abnormal DNA methylation and histone modification.89 These changes can occur

under certain environmental conditions, such as stroke,90 hypertension, type II

diabetes, obesity,91 exposure to heavy metals,92,93 head injury, immunological

proteins,94,95,96 and maternal viral infections.97-100 (see Epigenetic Modifications)

An analysis of the two largest GWAS available for AD has shown that there is a

significant overlap between disease-associated genes in pathways associated with

AD, cholesterol metabolism, and immune response.101 Neurodegeneration is often

accompanied by the accumulation of microglia and monocytes around amyloid

plaques and dying neurons.102 Furthermore, neurons express some molecules

normally attributed to the immune system, thus hinting at an intricate interplay

between neuronal and immune systems.103 (see Biology: Immunity)

ReviewsGuo J. L. and Lee V. M. (2014) Cell-to-cell transmission of pathogenic proteins in neurodegenerative diseases. Nat Med 20: 130-138

Medland S. E., Jahanshad N., Neale B. M. and Thompson P. M. (2014) Whole-genome analyses of whole-brain data: working within an expanded search space. Nat Neurosci 17: 791-800

Mitsui J. and Tsuji S. (2014) Genomic Aspects of Sporadic Neurodegenerative Diseases. Biochem Biophys Res Commun 452: 221-225

Jucker M. and Walker L. C. (2013) Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature 501: 45-51

Bras J., Guerreiro R. and Hardy J. (2012) Use of next-generation sequencing and other whole-genome strategies to dissect neurological disease. Nat Rev Neurosci 13: 453-464

Eisenberg D. and Jucker M. (2012) The amyloid state of proteins in human diseases. Cell 148: 1188-1203

Guerreiro R. J., Gustafson D. R. and Hardy J. (2012) The genetic architecture of Alzheimer's disease: beyond APP, PSENs and APOE. Neurobiol Aging 33: 437-456

Huang Y. and Mucke L. (2012) Alzheimer mechanisms and therapeutic strategies. Cell 148: 1204-1222

Mills J. D. and Janitz M. (2012) Alternative splicing of mRNA in the molecular pathology of neurodegenerative diseases. Neurobiol Aging 33: 1012 e1011-1024

Selkoe D. J. (2012) Preventing Alzheimer's disease. Science 337: 1488-1492

ReferencesCruchaga C., Karch C. M., Jin S. C., Benitez B. A., Cai Y., et al. (2014) Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer's disease. Nature 505: 550-554Research into LOAD has identified several genetic risk variants, but generally with small effects. To identify low-frequency coding variants with large effects, this study used WES on Illumina HiSeq in 14 large LOAD families and case-control data sets. The authors found a rare variant in PLD3 segregating with disease status in two independent families and doubling the risk for AD in seven independent case-control series. The authors conducted follow-up functional assays to determine the effect of PLD3 and found that it influences APP processing.

Illumina Technology: GoldenGate, HiSeq 2000

“Twin studies support the notion that epigenetic mechanisms modulate AD risk.” Huang and Mucke 2012

89. Chouliaras L., Rutten B. P., Kenis G., Peer-booms O., Visser P. J., et al. (2010) Epigenetic regulation in the pathophysiology of Alzhei-mer's disease. Prog Neurobiol 90: 498-510

90. Savva G. M., Stephan B. C. and Alzheimer's Society Vascular Dementia Systematic Review G. (2010) Epidemiological studies of the effect of stroke on incident dementia: a systematic review. Stroke 41: e41-46


92. Bakulski K. M., Rozek L. S., Dolinoy D. C., Paulson H. L. and Hu H. (2012) Alzheimer's disease and environmental exposure to lead: the epidemiologic evidence and potential role of epigenetics. Curr Alzheimer Res 9: 563-573

93. Basha M. R., Wei W., Bakheet S. A., Benitez N., Siddiqi H. K., et al. (2005) The fetal basis of amyloidogenesis: exposure to lead and latent overexpression of amyloid precursor protein and beta-amyloid in the aging brain. J Neurosci 25: 823-829

94. Gadad B. S., Hewitson L., Young K. A. and German D. C. (2013) Neuropathology and animal models of autism: genetic and environmental factors. Autism Res Treat 2013: 731935

95. Durkin M. S., Maenner M. J., Newschaffer C. J., Lee L. C., Cunniff C. M., et al. (2008) Advanced parental age and the risk of autism spectrum disorder. Am J Epidemiol 168: 1268-1276

96. Chung S. J., Armasu S. M., Anderson K. J., Biernacka J. M., Lesnick T. G., et al. (2013) Genetic susceptibility loci, environmental expo-sures, and Parkinson's disease: a case-con-trol study of gene-environment interactions. Parkinsonism Relat Disord 19: 595-599

97. Sun Y., Christensen J. and Olsen J. (2013) Childhood epilepsy and maternal antibodies to microbial and tissue antigens during pregnan-cy. Epilepsy Res 107: 61-74

98. Brown A. S. and Derkits E. J. (2010) Prenatal infection and schizophrenia: a review of epidemiologic and translational studies. Am J Psychiatry 167: 261-280

99. Torrey E. F. and Yolken R. H. (2003) Toxoplas-ma gondii and schizophrenia. Emerg Infect Dis 9: 1375-1380

100. Borglum A. D., Demontis D., Grove J., Pallesen J., Hollegaard M. V., et al. (2014) Genome-wide study of association and interaction with maternal cytomegalovirus infection suggests new schizophrenia loci. Mol Psychiatry 19: 325-333

101. Jones L., Harold D. and Williams J. (2010) Genetic evidence for the involvement of lipid metabolism in Alzheimer's disease. Biochim Biophys Acta 1801: 754-761

102. Lynch M. A. and Mills K. H. (2012) Immunol-ogy meets neuroscience--opportunities for immune intervention in neurodegenerative diseases. Brain Behav Immun 26: 1-10

103. Boulanger L. M. (2009) Immune proteins in brain development and synaptic plasticity. Neuron 64: 93-109


De Jager P. L., Srivastava G., Lunnon K., Burgess J., Schalkwyk L. C., et al. (2014) Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 17: 1156-1163DNA methylation is a genetic mechanism that may affect gene expression and, as such, may be implicated in disease susceptibility. The epigenomic influence on AD onset and progression was examined in this study using Illumina HumanMethylation450 arrays and bisulfite sequencing on Illumina HiSeq 2000. The authors found several replicated, functionally validated associations between altered DNA methylation and the pre-symptomatic accumulation of AD pathology. They hypothesized that the observed DNA methylation changes may be involved in the onset of AD.

Illumina Technology: HumanMethylation450, HiSeq 2000

Lunnon K., Smith R., Hannon E., De Jager P. L., Srivastava G., et al. (2014) Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease. Nat Neurosci 17: 1164-1170In this study of AD methylomic variation, the authors used Illumina HumanMethylation450k arrays to characterize the genome-wide DNA methylation state across multiple tissues. Based on the results from 122 donor samples, the authors compared the methylation state in four brain regions and whole blood where available. The authors identified evidence for cortex-specific hypermethylation at CpG sites in the ANK1 gene associated with AD neuropathology.

Illumina Technology: HumanMethylation450, Human Gene Expression BeadArray

Raj T., Ryan K. J., Replogle J. M., Chibnik L. B., Rosenkrantz L., et al. (2014) CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer's disease susceptibility. Hum Mol Genet 23: 2729-2736The identification of possible therapeutic targets for disease requires a functional identification follow-up to identified genetic disease variants. In this study, a previously identified risk allele for AD was scrutinized in detail using expression data on population cohorts stratified by their genetic risk variant identified by Illumina Infinium arrays. The authors found that the risk allele rs3865444(C) results in a higher surface density of CD33 on monocytes. The risk allele is strongly associated with greater expression of CD33 exon 2, which is likely to be the functional consequence of the risk variant.

Illumina Technology: Human OmniExpress

Bai B., Hales C. M., Chen P. C., Gozal Y., Dammer E. B., et al. (2013) U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease. Proc Natl Acad Sci U S A 110: 16562-16567Many neurodegenerative diseases are characterized by deposition of insoluble protein aggregates. The universal presence of ß-amyloid and tau proteins in AD has facilitated advancement of the amyloid cascade and tau hypotheses that have dominated AD pathogenesis research and therapeutic development. This study investigated the human brain-insoluble proteome in AD by mass spectrometry and transcriptome sequencing. The authors identified 36 proteins that accumulate in the disease and found similarities with protein aggregates in mild cognitive impairment.

Illumina Technology: HiSeq 2000 (mRNA sequencing)

Cruchaga C., Kauwe J. S., Harari O., Jin S. C., Cai Y., et al. (2013) GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease. Neuron 78: 256-268The progress of AD can be monitored by the tau phosphorylated threonine 181 (ptau) in cerebrospinal fluid (CSF). To identify the genetic mechanism associated with elevated ptau, the authors performed the largest GWAS to date, enrolling 1,269 participants. The participants were genotyped using Illumina OmniExpress arrays and tau/ptau levels measured. The authors identified three genome-wide significant loci for CSF tau and ptau; one of these showed a strong association with AD risk in independent data sets.

Illumina Technology: Human610-Quad, HumanOmniExpress

Lambert J. C., Ibrahim-Verbaas C. A., Harold D., Naj A. C., Sims R., et al. (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet 45: 1452-1458AD is a progressive neurological disorder, primarily affecting the elderly. Previous analyses have identified eleven genomic loci associated with LOAD. To search for additional risk loci, the authors conducted a large GWAS meta-analysis using published datasets of Illumina iSelect genotype data from ~17,000 AD cases and ~37,000 controls. The analysis resulted in 19 significant associated loci, of which 11 loci have not been associated previously with LOAD.

Illumina Technology: iSelect


Zhang B., Gaiteri C., Bodea L. G., Wang Z., McElwee J., et al. (2013) Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153: 707-720Despite decades of intensive research, the causal chain of mechanisms behind LOAD remains elusive. This study characterized the molecular systems associated with LOAD by RNA-Seq on Illumina HiSeq for both brain specimens and cell line samples. The authors built a rank-ordered molecular interaction network by LOAD pathology and identified an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis. The authors recommend the causal network structure as a useful predictor of response to gene perturbations and a framework to test models of disease mechanisms underlying LOAD.

Illumina Technology: HiSeq 2000 (mRNA sequencing), HT-12 Expression BeadChip, HumanHap 650Y

Sherva R., Tripodis Y., Bennett D. A., Chibnik L. B., Crane P. K., et al. (2014) Genome-wide association study of the rate of cognitive decline in Alzheimer's disease. Alzheimers Dement 10: 45-52

Guffanti G., Torri F., Rasmussen J., Clark A. P., Lakatos A., et al. (2013) Increased CNV-region deletions in mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects in the ADNI sample. Genomics 102: 112-122

Jonsson T., Atwal J. K., Steinberg S., Snaedal J., Jonsson P. V., et al. (2012) A mutation in APP protects against Alzheimer's disease and age-related cognitive decline. Nature 488: 96-99


Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative

disorder after AD.104 Approximately 1 million people in the U.S., and over 4 million

people world-wide, develop this pathological condition. The prevalence of PD in

industrialized countries is estimated at 1%–2% in people over 60 years of age, and

3%–5% in people over 85 years old. Only 1% of PD cases are familial; the rest are

sporadic.105 Typical symptoms include muscle rigidity, bradykinesia (slow movement),

tremors, and postural instability.106 As the disease progresses, memory loss can

occur and the symptoms may become very similar to those of AD.107 Persons

with PD may develop various neuropsychiatric disorders, such as anxiety, apathy,

depression, hallucinations, and delusions.108

The diagnosis of PD is based on symptoms, and no molecular tests are used at this

time.109 A majority of neurons exhibit degenerative dysfunction or are lost before the

onset of visible symptoms of PD,110 so early detection could improve the prognosis

substantially. Recently, a few groups have reported on the development of minimally

invasive diagnostic systems for detection of AD111,112,113 and PD114 in whole peripheral

blood, plasmacytoid bone marrow–derived cells (PBMCs), or CSF. Examples of

the target molecules for diagnostics include eukaryotic initiation factor 2 (EIF2),115

epidermal growth factor (EGF),116,117 and amyloid ß1-42.118,119


105. Mills J. D. and Janitz M. (2012) Alternative splicing of mRNA in the molecular pathology of neurodegenerative diseases. Neurobiol Aging 33: 1012 e1011-1024

106. de Lau L. M. and Breteler M. M. (2006) Epide-miology of Parkinson's disease. Lancet Neurol 5: 525-535


108. Emre M., Aarsland D., Brown R., Burn D. J., Duyckaerts C., et al. (2007) Clinical diagnostic criteria for dementia associated with Parkin-son's disease. Mov Disord 22: 1689-1707; quiz 1837

109. Fan H. C., Chen S. J., Harn H. J. and Lin S. Z. (2013) Parkinson's disease: from genetics to treatments. Cell Transplant 22: 639-652


111. Booij B. B., Lindahl T., Wetterberg P., Skaane N. V., Saebo S., et al. (2011) A gene expres-sion pattern in blood for the early detection of Alzheimer's disease. J Alzheimers Dis 23: 109-119

112. Rye P. D., Booij B. B., Grave G., Lindahl T., Kristiansen L., et al. (2011) A novel blood test for the early detection of Alzheimer's disease. J Alzheimers Dis 23: 121-129

113. Fehlbaum-Beurdeley P., Sol O., Desire L., Touchon J., Dantoine T., et al. (2012) Validation of AclarusDx, a blood-based transcriptomic signature for the diagnosis of Alzheimer's disease. J Alzheimers Dis 32: 169-181

114. Karlsson M. K., Sharma P., Aasly J., Toft M., Skogar O., et al. (2013) Found in transcription: accurate Parkinson's disease classification in peripheral blood. J Parkinsons Dis 3: 19-29

115. Mutez E., Nkiliza A., Belarbi K., de Broucker A., Vanbesien-Mailliot C., et al. (2014) Involve-ment of the immune system, endocytosis and EIF2 signaling in both genetically determined and sporadic forms of Parkinson's disease. Neurobiol Dis 63: 165-170

116. Chen-Plotkin A. S., Hu W. T., Siderowf A., Weintraub D., Goldmann Gross R., et al. (2011) Plasma epidermal growth factor levels predict cognitive decline in Parkinson disease. Ann Neurol 69: 655-663

117. Irwin D. J., Lee V. M. and Trojanowski J. Q. (2013) Parkinson's disease dementia: conver-gence of alpha-synuclein, tau and amyloid-be-ta pathologies. Nat Rev Neurosci 14: 626-636

118. Siderowf A., Xie S. X., Hurtig H., Weintraub D., Duda J., et al. (2010) CSF amyloid {beta} 1-42 predicts cognitive decline in Parkinson disease. Neurology 75: 1055-1061



The biological causes of PD include progressive loss of substantia nigra

dopaminergic neurons and striatal projections.120 The major pathological indicator of

PD is the accumulation of Lewy bodies, which are predominantly formed by self-

assembling small protein -synuclein expressed in multiple brain segments.121 Similar

to plaques in AD, Lewy bodies spread to other compartments of brain (e.g., limbic

and neocortical areas) as the disease progresses and cause neuronal death.122

Age is a major risk factor for development of PD123. Over 80% of PD patients will

eventually develop dementia, a condition termed Parkinson’s disease dementia

(PDD). It is believed that the major cause of dementia of this kind is the spread

of fibrillar α-synuclein from the brain stem to limbic and neocortical structures.124

Additionally, over 50% of PDD patients develop amyloid- ß plaques and neurofibrillary

tangles, typical for AD.125 Dual pathology of PDD and AD increases malignancy of the

disease and significantly worsens prognosis.

Mutations in six genes listed in Table 3 have been associated with PD.126,127

Table 3: Gene mutations associated with PD

Gene Name Protein Name Functional Role

SNCA α-synuclein Essential for normal brain activity: involved in learning, development, cellular differentiation, neuronal plasticity, and regulation of dopamine uptake.128,129,130 Risk factor both in familial and sporadic PD.131,132

PARK2 PARK2 (Parkin) Core component of a complex to ubiquitinate cellular proteins for degradation.133

PINK1 PTEN-induced kinase protein 1

Mitochondrially targeted kinase. Protects cells from stress-induced mitochondrial dysfunction, oxidative stress, and apoptosis.134

UCHL1 Ubiquitin carboxyl-terminal hydrolase izozyme L1

Neuronal-specific ubiquitin C-terminal hydrolase. Recycles ubiquitin chains back to monomeric ubiquitin and adds ubiquitin to monoubiquitylated α-synuclein.135

DJ1 (PARK7) PARK7 Modulates α-synuclein aggregation.136

LRRK2 Leucine-rich repeat serine/threonine protein kinase 2

LRRK2 may deregulate phosphorylation of α-synuclein, leading to initiation of PD pathogenesis.137









128. Clayton D. F. and George J. M. (1998) The synucleins: a family of proteins involved in synaptic function, plasticity, neurodegeneration and disease. Trends Neurosci 21: 249-254

129. Cooper A. A., Gitler A. D., Cashikar A., Haynes C. M., Hill K. J., et al. (2006) Alpha-synuclein blocks ER-Golgi traffic and Rab1 rescues neuron loss in Parkinson's models. Science 313: 324-328


131. Kirik D., Rosenblad C., Burger C., Lundberg C., Johansen T. E., et al. (2002) Parkinson-like neurodegeneration induced by targeted overexpression of alpha-synuclein in the nigrostriatal system. J Neurosci 22: 2780-2791

132. Baba M., Nakajo S., Tu P. H., Tomita T., Na-kaya K., et al. (1998) Aggregation of alpha-sy-nuclein in Lewy bodies of sporadic Parkinson's disease and dementia with Lewy bodies. Am J Pathol 152: 879-884


134. Deas E., Plun-Favreau H. and Wood N. W. (2009) PINK1 function in health and disease. EMBO Mol Med 1: 152-165

135. Cookson M. R. (2005) The biochemistry of Parkinson's disease. Annu Rev Biochem 74: 29-52

136. Zhou W., Bercury K., Cummiskey J., Luong N., Lebin J., et al. (2011) Phenylbutyrate up-regu-lates the DJ-1 protein and protects neurons in cell culture and in animal models of Parkinson disease. J Biol Chem 286: 14941-14951

137. Jowaed A., Schmitt I., Kaut O. and Wullner U. (2010) Methylation regulates alpha-synuclein expression and is decreased in Parkinson's disease patients' brains. J Neurosci 30: 6355-6359


The familial type of PD can be inherited in an autosomal dominant or autosomal

recessive manner. The former is associated with α-synuclein and LRRK2 and the

latter with Parkin, PINK1, DJ1, and ATP13A2. Improved understanding of the role

of these genes in PD will facilitate the development of genomic analysis as an early

diagnostic tool for familial PD.138

Currently, the major approach to controlling clinical symptoms of patients with

PD is pharmacological replacement of dopamine with L-DOPA, carbidopa, and

monoamine oxidase-B inhibitors.139 However, the effect of such treatment is transient,

and patients develop resistance to these therapies, leaving no further options for

treatment. Genomic analysis of PD is anticipated to improve treatment of PD. One

of the new therapeutic approaches made possible by NGS is based on the use

of mirtrons: miRNA relying on splicing, rather on Dicer and RNA-induced silencing

complex (RISC) to generate precursors for targeting disease-specific mRNA via an

RNA interference pathway.140 With this approach, researchers attained up to 85%

silencing of α-synuclein and LRRK2.

Methylation of α-synuclein was reduced in DNA from substantia nigra, cortex, and

putamen of patients with sporadic PD.141 Six risk loci have been associated with

proximal gene expression or DNA methylation142 (see Epigenetic Modifications).

ReviewsGuo J. L. and Lee V. M. (2014) Cell-to-cell transmission of pathogenic proteins in neurodegenerative diseases. Nat Med 20: 130-138

Sekiyama K., Takamatsu Y., Waragai M. and Hashimoto M. (2014) Role of genomics in translational research for Parkinson's disease. Biochem Biophys Res Commun 452: 226-235

Sharma M., Kruger R. and Gasser T. (2014) From genome-wide association studies to next-generation sequencing: lessons from the past and planning for the future. JAMA Neurol 71: 5-6

Fan H. C., Chen S. J., Harn H. J. and Lin S. Z. (2013) Parkinson's disease: from genetics to treatments. Cell Transplant 22: 639-652

Irwin D. J., Lee V. M. and Trojanowski J. Q. (2013) Parkinson's disease dementia: convergence of alpha-synuclein, tau and amyloid-beta pathologies. Nat Rev Neurosci 14: 626-636

Koboldt D. C., Steinberg K. M., Larson D. E., Wilson R. K. and Mardis E. R. (2013) The next-generation sequencing revolution and its impact on genomics. Cell 155: 27-38

Sandoe J. and Eggan K. (2013) Opportunities and challenges of pluripotent stem cell neurodegenerative disease models. Nat Neurosci 16: 780-789

Trinh J. and Farrer M. (2013) Advances in the genetics of Parkinson disease. Nat Rev Neurol 9: 445-454


138. Fan H. C., Chen S. J., Harn H. J. and Lin S. Z. (2013) Parkinson’s disease: from genetics to treatments. Cell Transplant 22: 639-652

139. Fan H. C., Chen S. J., Harn H. J. and Lin S. Z. (2013) Parkinson’s disease: from genetics to treatments. Cell Transplant 22: 639-652

140. Sibley C. R., Seow Y., Curtis H., Weinberg M. S. and Wood M. J. (2012) Silencing of Parkin-son's disease-associated genes with artificial mirtron mimics of miR-1224. Nucleic Acids Res 40: 9863-9875


142. Nalls M. A., Pankratz N., Lill C. M., Do C. B., Hernandez D. G., et al. (2014) Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease. Nat Genet 46: 989-993


ReferencesBras J., Guerreiro R., Darwent L., Parkkinen L., Ansorge O., et al. (2014) Genetic analysis implicates APOE, SNCA and suggests lysosomal dysfunction in the etiology of dementia with Lewy bodies. Hum Mol Genet Clinical and neuropathological similarities between dementia with Lewy bodies (DLB), PD, and AD, suggest that these disorders may share the same etiology. To test this hypothesis, this study used Illumina NeuroX custom arrays to test for association of 54 genomic regions, previously implicated in PD or AD in a large cohort of DLB cases and controls. The authors identified APOE as a strong genetic risk factor for DLB, and several other potential risk loci. Overall, the results suggest the etiology of DLB is influenced by some of the same genetic risk factors as AD and PD, but that these loci may act in different manners.

Illumina Technology: Infinium HumanExome BeadChip

Hollerhage M., Goebel J. N., de Andrade A., Hildebrandt T., Dolga A., et al. (2014) Trifluoperazine rescues human dopaminergic cells from wild-type alpha-synuclein-induced toxicity. Neurobiol Aging 35: 1700-1711PD is the most frequent neurodegenerative movement disorder. To study α-synuclein-mediated toxicity in PD progression, the authors developed a new cell-line model in which moderate overexpression of wild-type α-synuclein led to gradual death of human post-mitotic DA neurons. Using Illumina BeadArrays to monitor gene expression, the authors discovered that activating autophagy in human DA mid-brain neurons rescued them from α-synuclein-induced cell death. The phenothiazine neuroleptic trifluoperazine, an activator of macroautophagy, may be a potential therapeutic target.

Illumina Technology: Human Gene Expression BeadArray

Mencacci N. E., Isaias I. U., Reich M. M., Ganos C., Plagnol V., et al. (2014) Parkinson's disease in GTP cyclohydrolase 1 mutation carriers. Brain 137: 2480-2492GTP cyclohydrolase 1 (encoded by the GCH1 gene) is potentially associated with increased risk of PD. The frequency of GCH1 variants was evaluated in WES data of 1,318 cases with PD and 5,935 control subjects. Through their analysis, the authors identified 11 different heterozygous variants in GCH1. These include four previously reported pathogenic variants and seven variants of unknown clinical relevance. The frequency of GCH1 variants was significantly higher than in controls, indicating the clinical relevance of GTP cyclohydrolase 1 deficiency.

Illumina Technology: HiSeq 2000

Nalls M. A., Pankratz N., Lill C. M., Do C. B., Hernandez D. G., et al. (2014) Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease. Nat Genet 46: 989-993Only a small fraction of genetic heritability has been discovered for PD to date. This study performed a meta-analysis of GWAS for PD in the search for new loci associated with the disease. Using Illumina genotyping arrays, the authors identified 24 loci that were both statistically significant and replicated across experiments. Six of the identified loci had not been previously reported associated with PD, and the authors estimated the cumulative risk of the loci to be substantial (odds ratio = 3.31).

Illumina Technology: ExomeChip, HumanOmniExpress, HumanHap550, Human610-Quad, Human660W-Quad; HumanMethylation27, Human Gene Expression BeadArray

Chung S. J., Armasu S. M., Anderson K. J., Biernacka J. M., Lesnick T. G., et al. (2013) Genetic susceptibility loci, environmental exposures, and Parkinson's disease: a case-control study of gene-environment interactions. Parkinsonism Relat Disord 19: 595-599PD susceptibility is suspected to be a combination of genetic and environmental factors. This study examined the potential interactions between known genetic risk factors and environmental exposures (pesticide application, tobacco smoking, coffee drinking, and alcohol drinking). Using Illumina GoldenGate Genotyping arrays, the authors genotyped 1,098 PD cases and 1,098 controls, and performed an interaction analysis. They found limited evidence for pairwise interactions between the examined genotypes and environmental risk factors; however, larger sample sizes will be needed to confirm any effect on PD susceptibility.

Illumina Technology: BeadArray Reader, DNA Test Panel


Karlsson M. K., Sharma P., Aasly J., Toft M., Skogar O., et al. (2013) Found in transcription: accurate Parkinson's disease classification in peripheral blood. J Parkinsons Dis 3: 19-29The prevalence of PD increases with age. In an effort to develop a blood-based test for early detection of PD, the authors performed a large-scale gene expression study using Illumina Gene Expression BeadArrays on a cohort of PD patients and controls to develop a prediction model and test its accuracy. Through cross-validation results, they showed that PD can be correctly classified from healthy controls with an accuracy of 88% compared to clinical diagnosis. These results suggest the potential for developing a blood-based gene expression test for PD.


Ryan S. D., Dolatabadi N., Chan S. F., Zhang X., Akhtar M. W., et al. (2013) Isogenic human iPSC Parkinson's model shows nitrosative stress-induced dysfunction in MEF2-PGC1alpha transcription. Cell 155: 1351-1364An association has been reported between PD and exposure to mitochondrial toxins. In this study, a stem cell model was used to characterize the response to toxins using Illumina BeadArray for gene expression analysis. The authors identified a pathway whereby basal and toxin-induced nitrosative/oxidative stress results in S-nitrosylation of transcription factor MEF2C. They reported the alteration contributing to mitochondrial dysfunction and apoptotic cell death, indicating a mechanism and potential therapeutic target for PD.


Ganat Y. M., Calder E. L., Kriks S., Nelander J., Tu E. Y., et al. (2012) Identification of embryonic stem cell-derived midbrain dopaminergic neurons for engraftment. J Clin Invest 122: 2928-2939

Rousseaux M. W., Marcogliese P. C., Qu D., Hewitt S. J., Seang S., et al. (2012) Progressive dopaminergic cell loss with unilateral-to-bilateral progression in a genetic model of Parkinson disease. Proc Natl Acad Sci U S A 109: 15918-15923

Sibley C. R., Seow Y., Curtis H., Weinberg M. S. and Wood M. J. (2012) Silencing of Parkinson's disease-associated genes with artificial mirtron mimics of miR-1224. Nucleic Acids Res 40: 9863-9875


GENETIC MECHANISMS

Schizophrenia, autism, PD, and AD are complex diseases driven by an intricate

interplay between multiple genetic and environmental factors. The etiology of a

complex disease is the sum of all these factors, including somatic (non-inherited)

mutations, inherited mutations, epigenetic modifications, small RNAs, immunity, and

many others. NGS provides the tools to measure most of these contributing factors.

The future challenge will be to combine these measurements into a coherent view of

these complex diseases.143

Copy-Number Variants

CNVs are one of the most common mutations in psychiatric diseases. Some

notable successes have been achieved with array-based approaches, particularly

with mapping CNVs.144 However, arrays cannot detect balanced translocations

and fluorescence in situ hybridization (FISH) techniques have limited resolution. The

true extent of balanced translocations in both healthy and diseased genomes was

only discovered with the advent of NGS. Paired-end and mate-pair sequencing

are particularly effective in mapping genomic rearrangements.145 Two groups

have estimated that the number of CNVs relevant to ASD range from 130 to 300

target loci.146,147 CNVs also play a critical role in onset of schizophrenia and bipolar

disorder.148-151 A recent study of single cells and neurons in brain tissue found

that most (≥95%) neurons in normal brain tissue are euploid. However, a patient

with hemimegalencephaly (HMG) due to a somatic CNV of chromosome 1q had

unexpected tetrasomy 1q in 20% of neurons. This observation suggests that different

cells in the brain may have different mutations, and that CNVs in a minority of cells

can cause widespread brain dysfunction.152 This increased complexity can only be

resolved with single-cell sequencing approaches (see Biology: Single Cells).

The majority of de novo structural variations are attributed to new transposon

insertions. Exome sequencing studies have shown that an increased level of ASD-

causing single-nucleotide variants (SNVs) correlates positively with paternal age.153

CNVs can also contribute to disease, in combination with other CNVs or other

point mutations at different loci, but identifying these interactions is challenging.154

To account for these multiple impacts, several groups have devised a “two-hit”

hypothesis, analogous to that for cancer.155,156

“Recent advances in technology have allowed the interrogation of very large numbers of markers dispersed throughout the genome in a highly rapid and inexpensive manner.” Bras et al. 2012



145. Quinlan A. R. and Hall I. M. (2012) Characterizing complex structural variation in germline and somatic genomes. Trends Genet 28: 43-53

146. Levy D., Ronemus M., Yamrom B., Lee Y. H., Leotta A., et al. (2011) Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70: 886-897

147. Sanders S. J., Ercan-Sencicek A. G., Hus V., Luo R., Murtha M. T., et al. (2011) Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70: 863-885

148. Malhotra D., McCarthy S., Michaelson J. J., Vacic V., Burdick K. E., et al. (2011) High frequen-cies of de novo CNVs in bipolar disorder and schizophrenia. Neuron 72: 951-963

149. Priebe L., Degenhardt F. A., Herms S., Haenisch B., Mattheisen M., et al. (2012) Genome-wide survey implicates the influence of copy number variants (CNVs) in the development of early-onset bipolar disorder. Mol Psychiatry 17: 421-432

150. Zhang D., Cheng L., Qian Y., Alliey-Rodriguez N., Kelsoe J. R., et al. (2009) Singleton deletions throughout the genome increase risk of bipolar disorder. Mol Psychiatry 14: 376-380

151. Stefansson H., Meyer-Lindenberg A., Steinberg S., Magnusdottir B., Morgen K., et al. (2014) CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 505: 361-366

152. Cai X., Evrony G. D., Lehmann H. S., Elhosary P. C., Mehta B. K., et al. (2014) Single-Cell, Genome-wide Sequencing Identifies Clonal Somatic Copy-Number Variation in the Human Brain. Cell Rep 8: 1280-1289

153. Abnet C. C., Wang Z., Song X., Hu N., Zhou F. Y., et al. (2012) Genotypic variants at 2q33 and risk of esophageal squamous cell carcinoma in China: a meta-analysis of genome-wide associa-tion studies. Hum Mol Genet 21: 2132-2141



156. Gau S. S., Liao H. M., Hong C. C., Chien W. H. and Chen C. H. (2012) Identification of two inher-ited copy number variants in a male with autism supports two-hit and compound heterozygosity models of autism. Am J Med Genet B Neuropsy-chiatr Genet 159B: 710-717


Scandinavian families have been used extensively as subjects for autism and schizophrenia GWAS. This selection is due to the ethnic uniformity of Scandinavian peoples, and the well-developed registry system of newborn blood samples (such as Danish Newborn Screening Biobank) and health records established in those countries.157

ReviewsCai X., Evrony G. D., Lehmann H. S., Elhosary P. C., Mehta B. K., et al. (2014) Single-Cell, Genome-wide Sequencing Identifies Clonal Somatic Copy-Number Variation in the Human Brain. Cell Rep 8: 1280-1289

Gilissen C., Hehir-Kwa J. Y., Thung D. T., van de Vorst M., van Bon B. W., et al. (2014) Genome sequencing identifies major causes of severe intellectual disability. Nature 511: 344-347

Jacquemont S., Coe B. P., Hersch M., Duyzend M. H., Krumm N., et al. (2014) A higher mutational burden in females supports a "female protective model" in neurodevelopmental disorders. Am J Hum Genet 94: 415-425



ReferencesFromer M., Pocklington A. J., Kavanagh D. H., Williams H. J., Dwyer S., et al. (2014) De novo mutations in schizophrenia implicate synaptic networks. Nature 506: 179-184Of the known risk alleles for schizophrenia, the only ones definitively shown to confer considerable increments in risk are rare chromosomal CNVs that involve deletion or duplication of thousands of bases of DNA. This study examined the effect of small de novo mutations affecting one or a few nucleotides. By Illumina HiSeq WES of 623 schizophrenia trios, the authors assessed de novo mutation rates and shared genetic etiology for schizophrenia, intellectual disability, and ASDs. They found several insights to suggest a common etiological mechanism.

Illumina Technology: HiSeq (exome sequencing)

157. Szatkiewicz J. P., O'Dushlaine C., Chen G., Chambert K., Moran J. L., et al. (2014) Copy number variation in schizophrenia in Sweden. Mol Psychiatry 19: 762-773


Gaugler T., Klei L., Sanders S. J., Bodea C. A., Goldberg A. P., et al. (2014) Most genetic risk for autism resides with common variation. Nat Genet 46: 881-885Although ASDs have been studied widely, the proportion and nature of genetic heritability is uncertain. This study analyzed the largest autism cohort data to date, including 1.6 million Swedish families with at least two children and ~5,700 individuals with strict autism diagnoses. Using Illumina SNP arrays, the authors investigated the contribution of rare versus common genetic variants to the disease. They conclude that the heritability is ~52.4%, with common variation as the biggest contributor. Rare, de novo mutations contribute substantially to individual liability, yet their contribution to variance in liability is modest at 2.6%.

Illumina Technology: OmniExpress

Karayannis T., Au E., Patel J. C., Kruglikov I., Markx S., et al. (2014) Cntnap4 differentially contributes to GABAergic and dopaminergic synaptic transmission. Nature 511: 236-240In an effort to understand the pathological development of neurological disorders, genetic effects have been studied in the context of proteins that are expressed in neural cells. In this study, the authors characterized the effect of CNTNAP4 knockouts on mouse behavior and development, and relate these results to the findings of CNVs in humans across a region including the CNTNAP2 gene. The authors found that CNTNAP4 is localized presynaptically, and its loss leads to a reduction in the output of cortical parvalbumin (PV)-positive GABAergic basket cells. In addition, CNTNAP4-mutant mice showed defects in these neuronal populations and exhibited sensory-motor gating and grooming endophenotypes.

Illumina Technology: HumanHap550, HumanOmni1-Quad

Stefansson H., Meyer-Lindenberg A., Steinberg S., Magnusdottir B., Morgen K., et al. (2014) CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 505: 361-366Certain CNVs contribute to the pathogenesis of schizophrenia and autism. In this study, the authors investigated the influence of these CNVs on phenotypes separate from those of the mentioned diseases. In a big population-wide study of nearly a third of the Icelandic population (n = 101,655), the authors used Illumina SNP microarrays to test for associations of CNVs with cognitive deficits, dyslexia, dyscalculia, and brain structure changes. The authors found that the 15q11.2(BP1-BP2) deletion affects brain structure in a pattern consistent with first-episode psychosis in both schizophrenia and dyslexia.

Illumina Technology: HumanHap300, HumanCNV370-Duo, HumanHap650Y, Human1M, HumanOmni2.5, HumanOmniExpress, HumanOmni1S

Chapman J., Rees E., Harold D., Ivanov D., Gerrish A., et al. (2013) A genome-wide study shows a limited contribution of rare copy number variants to Alzheimer's disease risk. Hum Mol Genet 22: 816-824AD is the most common form of dementia, and although genetically complex, it is also highly heritable. This study examined the prevalence of CNVs using Illumina Infinium arrays to determine whether rare CNVs play a role in AD susceptibility. Although the authors examined loci that had been highlighted in previous AD studies, they did not find a significant contribution of CNVs to the development of AD.

Illumina Technology: Human610-Quad


McGrath L. M., Yu D., Marshall C., Davis L. K., Thiruvahindrapuram B., et al. (2014) Copy number variation in obsessive-compulsive disorder and tourette syndrome: a cross-disorder study. J Am Acad Child Adolesc Psychiatry 53: 910-919

Morris D. W., Pearson R. D., Cormican P., Kenny E. M., O'Dushlaine C. T., et al. (2014) An inherited duplication at the gene p21 Protein-Activated Kinase 7 (PAK7) is a risk factor for psychosis. Hum Mol Genet 23: 3316-3326


Ramos-Quiroga J. A., Sanchez-Mora C., Casas M., Garcia-Martinez I., Bosch R., et al. (2014) Genome-wide copy number variation analysis in adult attention-deficit and hyperactivity disorder. J Psychiatr Res 49: 60-67

Rees E., Kirov G., Sanders A., Walters J. T., Chambert K. D., et al. (2014) Evidence that duplications of 22q11.2 protect against schizophrenia. Mol Psychiatry 19: 37-40


Alternative Splicing

It has been established that up to 94% of multi-exon genes are alternatively spliced,

and that incorrect alternative splicing can lead to at least 15% of disease cases in

humans.158,159 Alternative splicing is a mechanism by which exons of pre-mRNA

can be grouped (spliced) into different arrangements to produce mature mRNA that

codes structurally and functionally distinct protein variants. The advent of high-

throughput genomic analysis tools, such as exon arrays and RNA-Seq, has allowed

the identification of alternative splicing events which were undetectable previously by

conventional microarrays.

Exon arrays can distinguish between different isoforms.160 This technology also has

some intrinsic limitations, such as the ability to detect only known splice variants of

previously sequenced genomes, low signal-to-noise ratio, limited dynamic range,

and cross-hybridization. The full power of this technology can be realized when

combined with whole mRNA sequencing, which allows the identification of the exon

and transcript boundaries at single-base resolution and detection of novel transcripts.

In this approach, the mRNA is first converted into cDNA, which is further ligated to

unique adapters and sequenced in a massively parallel fashion.161

Most genes commonly associated with AD have multiple splice variants, and some

of those variants are pathogenic.162,163,164 In PD, alternative splicing was detected

for PARK2, SNCA, and SRRM2 genes. Although all three splice variants of PARK2

are believed to be non-pathogenic, it was hypothesized that the variable ratio of

these three transcripts may determine disease susceptibility.165 Finally, there is

some evidence that unspliced mRNA corresponding to AD susceptibility genes can

accumulate in brains of AD patients as a result of mutations in U1 small nuclear

ribonucleoprotein (U1 snRNP), a component of the spliceosome complex. Multiple

U1 snRNP subunits form cytoplasmic tangled agglomerations in AD.166

RNA samples from human brains are usually acquired post-mortem, which raises

the problem of poor RNA quality for genetic analysis. However, correlative studies of

RNA and protein expression in brain and in peripheral organs and tissues, such as

blood, may provide the means for early, non-invasive diagnosis of neurodegenerative

diseases, similar to the approach once established for the diagnosis of

prostate cancer.167-170


159. Martin C. L., Duvall J. A., Ilkin Y., Simon J. S., Arreaza M. G., et al. (2007) Cytogenetic and molecular characterization of A2BP1/FOX1 as a candidate gene for autism. Am J Med Genet B Neuropsychiatr Genet 144B: 869-876




163. Raj T., Ryan K. J., Replogle J. M., Chibnik L. B., Rosenkrantz L., et al. (2014) CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer's disease suscepti-bility. Hum Mol Genet 23: 2729-2736

164. Bai B., Hales C. M., Chen P. C., Gozal Y., Dammer E. B., et al. (2013) U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease. Proc Natl Acad Sci U S A 110: 16562-16567


166. Bai B., Hales C. M., Chen P. C., Gozal Y., Dammer E. B., et al. (2013) U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease. Proc Natl Acad Sci U S A 110: 16562-16567

167. Carter H. B., Morrell C. H., Pearson J. D., Brant L. J., Plato C. C., et al. (1992) Estimation of prostatic growth using serial prostate-spe-cific antigen measurements in men with and without prostate disease. Cancer Res 52: 3323-3328

168. Carter H. B., Pearson J. D., Metter E. J., Brant L. J., Chan D. W., et al. (1992) Longitudinal evaluation of prostate-specific antigen levels in men with and without prostate disease. JAMA 267: 2215-2220


170. Mutez E., Nkiliza A., Belarbi K., de Broucker A., Vanbesien-Mailliot C., et al. (2014) Involve-ment of the immune system, endocytosis and EIF2 signaling in both genetically determined and sporadic forms of Parkinson's disease. Neurobiol Dis 63: 165-170


ReferencesAkula N., Barb J., Jiang X., Wendland J. R., Choi K. H., et al. (2014) RNA-sequencing of the brain transcriptome implicates dysregulation of neuroplasticity, circadian rhythms and GTPase binding in bipolar disorder. Mol Psychiatry. In press.

Fogel B. L., Cho E., Wahnich A., Gao F., Becherel O. J., et al. (2014) Mutation of senataxin alters disease-specific transcriptional networks in patients with ataxia with oculomotor apraxia type 2. Hum Mol Genet 23: 4758-4769

Gomez-Herreros F., Schuurs-Hoeijmakers J. H., McCormack M., Greally M. T., Rulten S., et al. (2014) TDP2 protects transcription from abortive topoisomerase activity and is required for normal neural function. Nat Genet 46: 516-521


Morihara T., Hayashi N., Yokokoji M., Akatsu H., Silverman M. A., et al. (2014) Transcriptome analysis of distinct mouse strains reveals kinesin light chain-1 splicing as an amyloid-beta accumulation modifier. Proc Natl Acad Sci U S A 111: 2638-2643

Norris A. D., Gao S., Norris M. L., Ray D., Ramani A. K., et al. (2014) A pair of RNA-binding proteins controls networks of splicing events contributing to specialization of neural cell types. Mol Cell 54: 946-959

Oldmeadow C., Mossman D., Evans T. J., Holliday E. G., Tooney P. A., et al. (2014) Combined analysis of exon splicing and genome wide polymorphism data predict schizophrenia risk loci. J Psychiatr Res 52: 44-49

Screen M., Jonson P. H., Raheem O., Palmio J., Laaksonen R., et al. (2014) Abnormal splicing of NEDD4 in myotonic dystrophy type 2: possible link to statin adverse reactions. Am J Pathol 184: 2322-2332


Epigenetic Modifications

The extensive role of epigenetic changes in the onset and progression of psychiatric

diseases has recently become apparent due to the development of microarray-

and NGS-based protocols for detecting genome-wide epigenetic patterns. This

role appeared to be especially strong in neurodegenerative diseases; however,

some recently discovered epigenetic patterns in psychiatric disorders may be

critical for better understanding the causes and patterns of these diseases. One of

the complications of studying the epigenetics in neurological diseases is that the

signature can only be detected post-mortem, and stability of samples and these

modifications is often compromised.171

Genomic imprinting is an example of an epigenetic modification that occurs

throughout life.172 SHANK3, the first gene associated with ASD, has five CpG

islands across the gene that display brain- and cell-type–specific DNA methylation

patterns.173,174,175 Similar specificity was observed for histone acetylation in this

gene.176 These modifications regulate the expression of the SHANK3 gene in an

isoform-specific manner.177 Several groups have independently identified other

multiple differentially methylated regions (DMRs) in post-mortem samples of autistic

patients representing biologically diverse gene regions, such as DNase-hypersensitive

sites and an alternative transcript termination site.178,179,180 These studies provide an

additional level of evidence to unravel the mechanisms of complex diseases,

such as ASD.



173. Beri S., Tonna N., Menozzi G., Bonaglia M. C., Sala C., et al. (2007) DNA methylation regulates tissue-specific expression of Shank3. J Neurochem 101: 1380-1391

174. Ching T. T., Maunakea A. K., Jun P., Hong C., Zardo G., et al. (2005) Epigenome analyses using BAC microarrays identify evolutionary conservation of tissue-specific methylation of SHANK3. Nat Genet 37: 645-651

175. Maunakea A. K., Nagarajan R. P., Bilenky M., Ballinger T. J., D'Souza C., et al. (2010) Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466: 253-257

176. Kriukiene E., Labrie V., Khare T., Urbanaviciute G., Lapinaite A., et al. (2013) DNA unmeth-ylome profiling by covalent capture of CpG sites. Nat Commun 4: 2190

177. Jiang Y. H. and Ehlers M. D. (2013) Modeling autism by SHANK gene mutations in mice. Neuron 78: 8-27





Aging and AD—in particular, LOAD—are also associated with a spectrum

of epigenetic changes, including abnormal DNA methylation and histone

modifications.181,182 These changes can be precipitated by physiological and

environmental conditions, such as stroke,183 hypertension, type II diabetes, obesity,184

exposure to heavy metals,185,186 and head injury.187 Oxidative stress, for example, can

cause an imbalance between methylation and demethylation of DNA in AD brains.188

Changes in histone tail modifications (primarily decreased levels of H3 acetylation in

the temporal lobe, and increased levels of histone deacetylases HDAC2 and HDAC6)

have also been observed in post-mortem brains of AD patients.189,190,191 Targeting

these histone modifications by therapeutic agents is a potential strategy for the

treatment of AD.192 Experiments in mouse have shown that pharmacological inhibition

of DNA methylation in the hippocampus of mice after a learning task impaired

memory consolidation,193 and promotion of histone acetylation had an opposite

effect: it increased learning and memory through increased learning-related gene

expression in aged mice.194,195

181. Chouliaras L., Rutten B. P., Kenis G., Peer-booms O., Visser P. J., et al. (2010) Epigenetic regulation in the pathophysiology of Alzhei-mer's disease. Prog Neurobiol 90: 498-510

182. Wang S. C., Oelze B. and Schumacher A. (2008) Age-specific epigenetic drift in late-on-set Alzheimer's disease. PLoS One 3: e2698

183. Savva G. M., Stephan B. C. and Alzheimer's Society Vascular Dementia Systematic Review G. (2010) Epidemiological studies of the effect of stroke on incident dementia: a systematic review. Stroke 41: e41-46




187. Vidaki A., Daniel B. and Court D. S. (2013) Forensic DNA methylation profiling--potential opportunities and challenges. Forensic Sci Int Genet 7: 499-507


189. Zhang K., Schrag M., Crofton A., Trivedi R., Vinters H., et al. (2012) Targeted proteomics for quantification of histone acetylation in Alz-heimer's disease. Proteomics 12: 1261-1268

190. Ding H., Dolan P. J. and Johnson G. V. (2008) Histone deacetylase 6 interacts with the micro-tubule-associated protein tau. J Neurochem 106: 2119-2130

191. Graff J., Rei D., Guan J. S., Wang W. Y., Seo J., et al. (2012) An epigenetic blockade of cognitive functions in the neurodegenerating brain. Nature 483: 222-226


193. Kilgore M., Miller C. A., Fass D. M., Hennig K. M., Haggarty S. J., et al. (2010) Inhibitors of class 1 histone deacetylases reverse contextual memory deficits in a mouse model of Alzheimer's disease. Neuropsychopharma-cology 35: 870-880

194. Fischer A., Sananbenesi F., Wang X., Dobbin M. and Tsai L. H. (2007) Recovery of learning and memory is associated with chromatin remodelling. Nature 447: 178-182

195. Peleg S., Sananbenesi F., Zovoilis A., Burkhardt S., Bahari-Javan S., et al. (2010) Altered histone acetylation is associated with age-dependent memory impairment in mice. Science 328: 753-756


Epigenetic modifications are also critical for the development of PD. Reduced

methylation levels of the SNCA gene that encodes α-synuclein protein were detected

in the substantia nigra, cortex, and putamen regions of brain in patients with sporadic

PD.196,197 The protein α-synuclein binds directly to histone H3 and inhibits

histone acetylation.198

ReviewsCoppede F. (2014) The potential of epigenetic therapies in neurodegenerative diseases. Front Genet 5: 220

ReferencesDe Jager P. L., Srivastava G., Lunnon K., Burgess J., Schalkwyk L. C., et al. (2014) Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 17: 1156-1163DNA methylation is a genetic mechanism that may affect gene expression and, as such, may be implicated in disease susceptibility. The epigenomic influence on AD onset and progression was examined in this study using Illumina HumanMethylation450 arrays and bisulfite sequencing on Illumina HiSeq 2000. The authors found several replicated, functionally validated associations between altered DNA methylation and the pre-symptomatic accumulation of AD pathology. They hypothesized that the observed DNA methylation changes may be involved in the onset of AD.


Hollerhage M., Goebel J. N., de Andrade A., Hildebrandt T., Dolga A., et al. (2014) Trifluoperazine rescues human dopaminergic cells from wild-type alpha-synuclein-induced toxicity. Neurobiol Aging 35: 1700-1711PD is the most frequent neurodegenerative movement disorder. To study α-synuclein-mediated toxicity in PD progression, the authors developed a new cell-line model in which moderate overexpression of wild-type α-synuclein led to gradual death of human post-mitotic dopaminergic neurons. Using Illumina BeadArrays to monitor gene expression, the authors discovered that activating autophagy in human dopaminergic mid-brain neurons rescued them fromα-synuclein-induced cell death. The phenothiazine neuroleptic trifluoperazine, an activator of macroautophagy, may be a potential therapeutic target.


Ladd-Acosta C., Hansen K. D., Briem E., Fallin M. D., Kaufmann W. E., et al. (2014) Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry 19: 862-871Genetic as well as environmental factors are responsible for ASDs. In this study, the authors measured over 485,000 CpG loci in human brain tissue and identified 4 genome-wide significantly different DMRs from 19 autism cases. This study highlights a new selected set of affected genes.

Illumina Technology: HumanMethylation450

Lunnon K., Smith R., Hannon E., De Jager P. L., Srivastava G., et al. (2014) Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease. Nat Neurosci 17: 1164-1170In this study of AD methylomic variation, the authors used Illumina HumanMethylation450k arrays to characterize the genome-wide DNA methylation state across multiple tissues. Based on the results from 122 donor samples, the authors compared the methylation state in four brain regions and whole blood where available. The authors identified evidence for cortex-specific hypermethylation at CpG sites in the ANK1 gene associated with AD neuropathology.

Illumina Technology: HumanMethylation450k, Human Gene Expression BeadArray


197. Matsumoto L., Takuma H., Tamaoka A., Kuri-saki H., Date H., et al. (2010) CpG demeth-ylation enhances alpha-synuclein expression and affects the pathogenesis of Parkinson's disease. PLoS One 5: e15522







Small RNAs

MicroRNA (miRNA) is enriched in the brain, and neuronal-specific miRNA controls

neuronal differentiation, excitability, and function.199 Other RNAs, such as non-coding

RNA (ncRNA), appear to play a role in neurodevelopment.

A number of miRNAs are associated with AD and PD, and were detected not only

in brains, but also in peripheral tissues of affected individuals.200,201 This observation

suggests that minimally invasive diagnostic tools could be developed for early

prediction of neurodegenerative disorders. miRNAs have also been considered as

therapeutic agents against AD pathogens, such as APP202. miRNA molecules can

be conjugated to—or otherwise associated with—aptamers, monoclonal antibodies,

peptides, or exosomes for delivery in vivo to specific cell types and tissues.203

ReviewsSekiyama K., Takamatsu Y., Waragai M. and Hashimoto M. (2014) Role of genomics in translational research for Parkinson's disease. Biochem Biophys Res Commun 452: 226-235

Maciotta S., Meregalli M. and Torrente Y. (2013) The involvement of microRNAs in neurodegenerative diseases. Front Cell Neurosci 7: 265


ReferencesRaj T., Ryan K. J., Replogle J. M., Chibnik L. B., Rosenkrantz L., et al. (2014) CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer's disease susceptibility. Hum Mol Genet 23: 2729-2736The identification of possible therapeutic targets requires a functional identification follow-up to known genetic disease variants. In this study, a previously identified risk allele for AD was examined in detail. The authors used expression data in population cohorts stratified by their genetic risk variant, identified by Illumina Infinium arrays. They found that the risk allele rs3865444(C) results in a higher surface density of CD33 on monocytes. The risk allele is strongly associated with greater expression of CD33 exon 2, which is likely to be the functional consequence of the risk variant.

Illumina Technology: OmniExpress

Bai B., Hales C. M., Chen P. C., Gozal Y., Dammer E. B., et al. (2013) U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease. Proc Natl Acad Sci U S A 110: 16562-16567Many neurodegenerative diseases are characterized by deposition of insoluble protein aggregates. The universal presence of ß-amyloid and tau proteins in AD has facilitated advancement of the amyloid cascade and tau hypotheses that have dominated AD pathogenesis research and therapeutic development. This study investigated the human brain-insoluble proteome in AD by mass spectrometry and transcriptome sequencing. The authors identified 36 proteins that accumulate in the disease and found similarities with protein aggregates in mild cognitive impairment.

Illumina Technology: HiSeq 2000 (mRNA sequencing)

Li M. M., Jiang T., Sun Z., Zhang Q., Tan C. C., et al. (2014) Genome-wide microRNA expression profiles in hippocampus of rats with chronic temporal lobe epilepsy. Sci Rep 4: 4734

199. Maciotta S., Meregalli M. and Torrente Y. (2013) The involvement of microRNAs in neu-rodegenerative diseases. Front Cell Neurosci 7: 265


201. Sekiyama K., Takamatsu Y., Waragai M. and Hashimoto M. (2014) Role of genomics in translational research for Parkinson's disease. Biochem Biophys Res Commun 452: 226-235




Genetic Variants

Genome Wide Association StudiesGenome-wide association studies (GWAS) are conducted to identify common

disease susceptibility alleles by comparing allele frequencies across the genome

in large groups of cases and controls.204,205 This approach has yielded an

unprecedented amount of clinically relevant data, including the majority of mutations

and variants associated with AD and PD.207-211 However, in spite of over 9,000 GWAS

published to date, this approach has uncovered only a fraction of the true heritability.

Additionally, GWAS can miss epigenetic patterns, such as methylation, and it

can mistakenly identify genes in the vicinity of pathogenic213 SNVs as pathogenic.

The sequencing of entire genomes in large cohorts at affordable prices is likely to

generate additional genes, pathways, and biological insights, as well as the potential

to identify causal mutations.214 NGS, both alone or in combination with microarrays,

can address most of these limitations and significantly improve the results from

these studies.215

ReviewsSharma M., Kruger R. and Gasser T. (2014) From genome-wide association studies to next-generation sequencing: lessons from the past and planning for the future. JAMA Neurol 71: 5-6

Keogh M. J. and Chinnery P. F. (2013) Next generation sequencing for neurological diseases: new hope or new hype? Clin Neurol Neurosurg 115: 948-953e>


ReferencesKarayannis T., Au E., Patel J. C., Kruglikov I., Markx S., et al. (2014) Cntnap4 differentially contributes to GABAergic and dopaminergic synaptic transmission. Nature 511: 236-240In an effort to understand the pathological development of neurological disorders, genetic effects have been studied in the context of proteins that are expressed in neural cells. In this study, the authors characterized the effect of CNTNAP4 knockouts on mouse behavior and development, and relate these results to the findings of CNVs in humans across a region including the CNTNAP2 gene. The authors found that Cntnap4 is localized presynaptically, and its loss leads to a reduction in the output of cortical parvalbumin (PV)-positive GABAergic basket cells. In addition, CNTNAP4-mutant mice showed defects in these neuronal populations and exhibited sensory-motor gating and grooming endophenotypes.

Illumina Technology: HumanHap550, HumanOmni1




205. Keogh M. J. and Chinnery P. F. (2013) Next generation sequencing for neurological diseases: new hope or new hype? Clin Neurol Neurosurg 115: 948-953e>

206. Sharma M., Kruger R. and Gasser T. (2014) From genome-wide association studies to next-generation sequencing: lessons from the past and planning for the future. JAMA Neurol 71: 5-6

207. Harold D., Abraham R., Hollingworth P., Sims R., Gerrish A., et al. (2009) Genome-wide as-sociation study identifies variants at CLU and PICALM associated with Alzheimer's disease. Nat Genet 41: 1088-1093

208. Hollingworth P., Harold D., Sims R., Gerrish A., Lambert J. C., et al. (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease. Nat Genet 43: 429-435

209. Lambert J. C., Heath S., Even G., Campion D., Sleegers K., et al. (2009) Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease. Nat Genet 41: 1094-1099

210. Naj A. C., Jun G., Beecham G. W., Wang L. S., Vardarajan B. N., et al. (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nat Genet 43: 436-441



213. Sekiyama K., Takamatsu Y., Waragai M. and Hashimoto M. (2014) Role of genomics in translational research for Parkinson's disease. Biochem Biophys Res Commun

214. Visscher P. M., Brown M. A., McCarthy M. I. and Yang J. (2012) Five years of GWAS discovery. Am J Hum Genet 90: 7-24



Cruchaga C., Kauwe J. S., Harari O., Jin S. C., Cai Y., et al. (2013) GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease. Neuron 78: 256-268The progress of AD can be monitored by the tau phosphorylated threonine 181 (ptau) in CSF. To identify the genetic mechanism associated with elevated ptau, the authors performed the largest GWAS to date, enrolling 1,269 participants. The participants were genotyped using Illumina OmniExpress arrays and tau/ptau levels measured. The authors identified three genome-wide significant loci for CSF tau and ptau; one of these showed a strong association with AD risk in independent data sets.

Illumina Technology: Human610-Quad, HumanOmniExpress

Next-Generation SequencingNext-generation sequencing for the analysis of eukaryotic DNA genome consists

of two modalities: WGS and WES.216 A combination of WES with custom-designed

microarrays is the method of choice for large sample sizes.217 This combinatory

approach allows the effective resolution of such common genetic analysis

problems as pseudogenes, repeated exons, and a failure to detect rare and/

or novel mutations.218 Additionally, WES per se is not very adequate currently for

addressing CNVs, because sample preparation relies on non-quantitative PCR

amplification.219 However, an approach that combines WES with genotyping resolves

this complication.220

A multi-pronged approach—supplementing WES with WGS, proteomics, and

epigenomics—is anticipated to deliver full understanding of the effects of newly

discovered genetic variability. Rare mutations often have significantly stronger effects

on disease pathology than common mutations, and that effect has been especially

remarkable in the field of complex diseases. The power of NGS was further proved in

the discovery of de novo mutations, which arise in individuals during their life and are

more likely to have functional roles in rare diseases. Although the appearance of such

mutations seems stochastic, the mutation rate and its dependence on parental age222

and other environmental factors are only some of the important results of using

this technology.223

Sequencing is becoming indispensable for diagnosing disease by analyzing

circulating tumor DNA. Not only the sequence of that DNA, but also fluctuations in

cell count, often correlate with the disease pathology and state. The “liquid biopsy

approach” allows the detection of somatic mutations for a specific tumor type in

plasma or liquid Papanicolaou (Pap) smears.224,225 This approach is currently being

developed to detect PD226 and AD227,228 in peripheral blood. The recent discovery

that the whole fetal genome is present in maternal plasma during pregnancy230 has

launched the era of pre-natal non-invasive genetic diagnostics.

“Unlike GWAS which examines common mutations, sequencing facilitates the discovery of rare mutations which often associate with complex phenotypes.”Koboldt et al. 2013

216. Medland S. E., Jahanshad N., Neale B. M. and Thompson P. M. (2014) Whole-genome analyses of whole-brain data: working within an expanded search space. Nat Neurosci 17: 791-800






222. Kong A., Frigge M. L., Masson G., Besen-bacher S., Sulem P., et al. (2012) Rate of de novo mutations and the importance of father's age to disease risk. Nature 488: 471-475


224. Forshew T., Murtaza M., Parkinson C., Gale D., Tsui D. W., et al. (2012) Noninvasive identi-fication and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med 4: 136ra168

225. Kinde I., Bettegowda C., Wang Y., Wu J., Agrawal N., et al. (2013) Evaluation of DNA from the Papanicolaou test to detect ovarian and endometrial cancers. Sci Transl Med 5: 167ra164

226. Karlsson M. K., Sharma P., Aasly J., Toft M., Skogar O., et al. (2013) Found in transcription: accurate Parkinson's disease classification in peripheral blood. J Parkinsons Dis 3: 19-29

227. Booij B. B., Lindahl T., Wetterberg P., Skaane N. V., Saebo S., et al. (2011) A gene expres-sion pattern in blood for the early detection of Alzheimer's disease. J Alzheimers Dis 23: 109-119

228. Rye P. D., Booij B. B., Grave G., Lindahl T., Kristiansen L., et al. (2011) A novel blood test for the early detection of Alzheimer's disease. J Alzheimers Dis 23: 121-129

229. Fehlbaum-Beurdeley P., Sol O., Desire L., Touchon J., Dantoine T., et al. (2012) Validation of AclarusDx, a blood-based transcriptomic signature for the diagnosis of Alzheimer's disease. J Alzheimers Dis 32: 169-181

230. Lo Y. M., Chan K. C., Sun H., Chen E. Z., Jiang P., et al. (2010) Maternal plasma DNA sequencing reveals the genome-wide genetic and mutational profile of the fetus. Sci Transl Med 2: 61ra91


ReviewsRabbani B., Tekin M. and Mahdieh N. (2014) The promise of whole-exome sequencing in medical genetics. J Hum Genet 59: 5-15




ReferencesDe Jager P. L., Srivastava G., Lunnon K., Burgess J., Schalkwyk L. C., et al. (2014) Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 17: 1156-1163DNA methylation is a genetic mechanism that may affect gene expression and, as such, may be implicated in disease susceptibility. The epigenomic influence on AD onset and progression was examined in this study using Illumina HumanMethylation450 arrays and bisulfite sequencing on Illumina HiSeq 2000. The authors found several replicated, functionally validated associations between altered DNA methylation and the pre-symptomatic accumulation of AD pathology. They hypothesized that the observed DNA methylation changes may be involved in the onset of AD.


Fromer M., Pocklington A. J., Kavanagh D. H., Williams H. J., Dwyer S., et al. (2014) De novo mutations in schizophrenia implicate synaptic networks. Nature 506: 179-184Of the known risk alleles for schizophrenia, the only ones definitively shown to confer considerable increments in risk are rare chromosomal CNVs that involve deletion or duplication of thousands of bases of DNA. This study examined the effect of small de novo mutations affecting one or a few nucleotides. By Illumina HiSeq WES of 623 schizophrenia trios, the authors assessed de novo mutation rates and shared genetic etiology for schizophrenia, intellectual disability, and ASDs. They found several insights to suggest a common etiological mechanism.

Illumina Technology: HiSeq (exome sequencing)

Lee H., Lin M. C., Kornblum H. I., Papazian D. M. and Nelson S. F. (2014) Exome sequencing identifies de novo gain of function missense mutation in KCND2 in identical twins with autism and seizures that slows potassium channel inactivation. Hum Mol Genet 23: 3481-3489Numerous studies have reported comorbidity of autism and epilepsy, but the relationship between the two disorders is unknown. In this study, identical twins, affected by both autism and severe intractable seizures, were studied using exome sequencing. A novel variant in the KCND2 gene was observed in both twins. The de novo mutation is located in the protein coding the Kv4.2 potassium channel, and the authors expressed the mutant protein in Xenopus oocytes to observe functional effects. Expression analysis showed that the mutation dominantly impairs the closed-state inactivation of the potassium channel, strongly supporting KCND2 as the causal gene for epilepsy in this family.

Illumina Technology: HiSeq 2000, Illumina Paired-End Sequencing Library Prep Protocol

King I. F., Yandava C. N., Mabb A. M., Hsiao J. S., Huang H. S., et al. (2013) Topoisomerases facilitate transcription of long genes linked to autism. Nature 501: 58-62Topoisomerases are expressed throughout the developing and adult brain, and are mutated in some individuals with ASD. However, the mechanism by which topoisomerases impact ASD is unknown. By transcriptome sequencing, in combination with genome-wide mapping of RNA polymerase II density in neurons, the authors found that expression of long genes was reduced after knockdown of topoisomerase in neurons. The authors noted that many high-confidence ASD candidate genes are exceptionally long and were reduced in expression after TOP1 inhibition. This observation suggests that topoisomerases could contribute commonly to ASD.

Illumina Technology: HiSeq 2000, TruSeq RNA, TruSeq for ChIP-Seq


Willsey A. J., Sanders S. J., Li M., Dong S., Tebbenkamp A. T., et al. (2013) Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155: 997-1007Recent studies employing WES and WGS have identified nine high-confidence ASD genes. This study examined the contribution of these nine genes to the common phenotype by combining Illumina WES and RNA-Seq data into co-expression networks. The authors explain how these networks will guide future ASD research by indicating which genes are most likely to have overlapping molecular, cellular or circuit-level phenotypes.

Illumina Technology: HiSeq 2000, Genome Analyzer

Yu T. W., Chahrour M. H., Coulter M. E., Jiralerspong S., Okamura-Ikeda K., et al. (2013) Using whole-exome sequencing to identify inherited causes of autism. Neuron 77: 259-273Steel syndrome is a developmental structural disorder first described in 1993 in 23 Hispanic children from Puerto Rico. This paper presents the genomic analysis of a family with two affected siblings. The authors used whole-exome sequencing using the Baylor College of Medicine Human Genome Sequencing Center (BCM-HGSC) core design followed by sequencing of both affected siblings, parents, and an affected cousin and her unaffected parents. By filtering the detected genetic variants by segregation, the authors discovered a single homozygous missense variant segregating with the disorder. The variant disrupts the collagen gene COL27A1, which codes for a protein expressed in developing cartilage.

Illumina Technology: HiSeq 2000, HumanOmniExpress


MODEL SYSTEMS

Modeling of neurological diseases has been challenging for two primary reasons:

extremely limited access to the primary brain tissue of individuals affected by these

diseases, and polygenicity of these disorders. Traditional knockout models reiterate

only a fraction of the disease phenotype, leaving room for speculation about the

relevance of research results to real human diseases. The introduction of triple-

knockout mice, transgenic rats, and stem cells as in vitro models has significantly

broadened the arsenal of tools available to researchers in the field and moved closer

to the “hypothetical” ideal disease model.231 The development of adequate model

systems is essential for developing accurate diagnostic and therapeutic strategies.

Animal Models

The development of animal models for schizophrenia and ASD has been challenging,

because over 90% of these diseases are polygenic. Therefore, a standard single-

knockout mouse model can only partially mimic the disease phenotype. Introduced

mutations are only tangentially relevant to the disease, and symptoms observed in

animals may represent other diseases in this spectrum.232 Another complication is

associated with the recognition and quantification of the symptoms: animal behavioral

patterns are different from those of a human; therefore, interpretation of behavioral

changes, feelings, and intentions of animals may be highly subjective.

SchizophreniaRodents have been the most commonly used models for studying schizophrenia and

ASD. Until recently, these models were limited to mice, but now some rat knockouts

have also become available.233 Rats may be beneficial over mice, as they are highly

social animals and possess a rich acoustic communication system (including

frequencies in the ultrasonic range) and have a closer resemblance to human neural

processes.234 Additionally, pre-clinical toxicology studies are normally carried out in

rats; therefore, use of these animals as a research model can significantly streamline

the drug development process.235

“Given the low penetrance of schizophrenia-associated alleles, and their ability to contribute to different diseases, inserting one or even several into an animal model may yield a phenotype that is ambiguous with respect to human disease — or no phenotype at all.” Hyman 2014

231. Lyons M. R. and West A. E. (2011) Mecha-nisms of specificity in neuronal activity-regulat-ed gene transcription. Prog Neurobiol 94: 259-295

232. Kvajo M., McKellar H. and Gogos J. A. (2012) Avoiding mouse traps in schizophrenia genet-ics: lessons and promises from current and emerging mouse models. Neuroscience 211: 136-164

233. Wohr M. and Scattoni M. L. (2013) Behavioural methods used in rodent models of autism spectrum disorders: current standards and new developments. Behav Brain Res 251: 5-17

234. Lipska B. K. and Weinberger D. R. (2000) To model a psychiatric disorder in animals: schizophrenia as a reality test. Neuropsycho-pharmacology 23: 223-239

235. Wohr M. and Scattoni M. L. (2013) Behavioural methods used in rodent models of autism spectrum disorders: current standards and new developments. Behav Brain Res 251: 5-17


Several genetic mouse models of schizophrenia have been developed that use

one of the three traditional methods: conventional gene targeting, conditional gene

targeting, or point mutation by chemical mutagens.236 However, these techniques

allow for generation of phenotypes only scarcely similar to schizophrenia. For that

reason, Cre/loxP-based chromosome engineering technique was used to generate

models reflecting complex genomic rearrangements, such as large deletions,

inversions, and duplications.237

One of the oldest models of schizophrenia is the dominant-negative disrupted in

schizophrenia 1 (DISC1) gene. DISC1 mice are a good model for studying not only

schizophrenia, but also a dual diagnosis of schizophrenia and substance-abuse

disorder.238 The effects of this mutation on brain structure or function remain to

be studied.239

Autism Spectrum DisorderMouse models have allowed for the demonstration of fundamental principles of ASD

diseases. Mouse knockout models, with mutations in a range of genes, are making

a significant contribution to understanding disease onset. These genes include

SHANK3 (Phelan-McDermid syndrome, idiopatic ASD), MeCP2 (Rett syndrome),

Fragile X chromosome (FMR1), PTEN (autism), and others.240,241 SHANK3 is a good

example of the importance of reproducing the exact type and point of mutation of the

gene: some mutations in this gene are also associated with other diseases, including

schizophrenia and intellectual disability.242,243 Microduplications of SHANK3 were

also associated with developmental delay and dysmorphic features in children.244

This phenomenon underpins the need to use genomic analysis tools for accurate

identification of mutations in spectrum disorders, and for the verification of their

accurate reproduction in animal models.

Additional models of ASD include non-human primates, songbirds, zebrafish,

Drosophila, and C. elegans. Non-human primates have been instrumental in studying

the behavioral patterns in this disorder, as the anatomy of their neural circuits

responsible for mediating social behavior is very similar to that of humans.245 Like

humans, they possess mirror neurons—cells responsible for repeating the actions of

others, commonly damaged in autism. For ethical reasons, introduction of genetic

mutations into primates is currently not feasible.

Songbirds have been used as a model due to their well-developed vocal machinery.

As in humans, vocal learning is an important element of language in this species, and

its impairment is commonly associated with ASD.246 Finally, zebrafish, Drosophila,

and C. elegans have been used extensively to study the genetic fundamentals of

psychiatric diseases.247

236. Nomura J. and Takumi T. (2012) Animal models of psychiatric disorders that reflect human copy number variation. Neural Plast 2012: 589524

237. Nomura J. and Takumi T. (2012) Animal models of psychiatric disorders that reflect human copy number variation. Neural Plast 2012: 589524

238. Ng E., McGirr A., Wong A. H. and Roder J. C. (2013) Using rodents to model schizophrenia and substance use comorbidity. Neurosci Biobehav Rev 37: 896-910

239. Kvajo M., McKellar H. and Gogos J. A. (2012) Avoiding mouse traps in schizophrenia genetics: lessons and promises from current and emerging mouse models. Neuroscience 211: 136-164


241. Gadad B. S., Hewitson L., Young K. A. and German D. C. (2013) Neuropathology and animal models of autism: genetic and environ-mental factors. Autism Res Treat 2013: 731935

242. Gauthier J., Champagne N., Lafreniere R. G., Xiong L., Spiegelman D., et al. (2010) De novo mutations in the gene encoding the synaptic scaffolding protein SHANK3 in patients ascer-tained for schizophrenia. Proc Natl Acad Sci U S A 107: 7863-7868

243. Tucker T., Zahir F. R., Griffith M., Delaney A., Chai D., et al. (2014) Single exon-resolution targeted chromosomal microarray analysis of known and candidate intellectual disability genes. Eur J Hum Genet 22: 792-800

244. Okamoto N., Kubota T., Nakamura Y., Murakami R., Nishikubo T., et al. (2007) 22q13 Microduplication in two patients with common clinical manifestations: a recognizable syn-drome? Am J Med Genet A 143A: 2804-2809

245. Watson K. K. and Platt M. L. (2012) Of mice and monkeys: using non-human primate models to bridge mouse- and human-based investigations of autism spectrum disorders. J Neurodev Disord 4: 21

246. Clayton D. F., Balakrishnan C. N. and London S. E. (2009) Integrating genomes, brain and behavior in the study of songbirds. Curr Biol 19: R865-873



Animal models used in research of psychiatric and neurodegenerative diseases: mice, rats, non-human primates, Drosophila, songbirds, and zebrafish.

Alzheimer’s DiseaseAnimal models of AD are challenging to develop, as spontaneous amyloidosis is not

common in laboratory animals. Aged non-human primates can develop ß-amyloidosis

and tau fibrillary inclusions; however, these animals do not develop the clinical signs

of human AD.248 Currently used animal models of AD are mostly limited to genetically

engineered mice.249 These models allowed for the successful mimicking of most

human cerebral amyloidosis, including ß-amyloidosis and taupathies.250 In all cases,

overexpression of human amyloidogenic protein was required.251

A mouse model overexpressing ß-amyloid protein has also been established.

Although neurofibrillary tangles are not produced in brains of these mice, tau

pathology is still observed, because ß-amyloid pathology activates kinases,

down-regulates phosphatases, and impairs tau degradation.252 Mice expressing

both mutated APP and tau demonstrate greater neurofibrillary tangle pathology

than mutated tau mice, thus suggesting a role of ß-amyloid accumulation in the

development of tau pathology.253

Apart from the previously mentioned models, knockout models for genes involved

in APP processing—such as presenelin-1, presenelin-2, and b-secretase enzyme

(BACE1) are now available. Unfortunately, none of them accurately mimics all the key

symptoms and molecular signatures of this disease. Specifically, increased neuronal

death has been one of such symptoms, and it seems to be necessary for selecting

and testing drugs against AD. A more advanced model was obtained by crossing

APP-overexpressing mice with transgenic animals expressing mutated presenilin-1

248. Jucker M. (2010) The benefits and limitations of animal models for translational research in neurodegenerative diseases. Nat Med 16: 1210-1214

249. Eisenberg D. and Jucker M. (2012) The amy-loid state of proteins in human diseases. Cell 148: 1188-1203



252. Blurton-Jones M. and Laferla F. M. (2006) Pathways by which Abeta facilitates tau pa-thology. Curr Alzheimer Res 3: 437-448

253. Lewis J., Dickson D. W., Lin W. L., Chisholm L., Corral A., et al. (2001) Enhanced neu-rofibrillary degeneration in transgenic mice expressing mutant tau and APP. Science 293: 1487-1491

254. Oddo S., Caccamo A., Shepherd J. D., Murphy M. P., Golde T. E., et al. (2003) Triple-transgenic model of Alzheimer's disease with plaques and tangles: intracellular Abeta and synaptic dysfunction. Neuron 39: 409-421


or presenelin-2, with the third transgene (mutated tau) added to the system. These

mice featured accelerated ß-amyloid pathology, formation of neurofibrillary tangles,

neuronal loss and cognitive decline, and tau pathology. 254

Although significant progress has been made in the development of mouse models

of AD, the available models do not take into account genetic and epigenetic variability

or the immunological factors common in LOAD. Recent advances in NGS technology

are anticipated to intensify building of these models, which are essential to develop

disease-modifying drugs for AD.255

Parkinson’s diseaseThe etiopathogenesis of PD is not yet clarified, and existing animal models have many

limitations. Nevertheless, they allow for unraveling some fundamental mechanisms

underlying the molecular and cellular basis of this neurodegenerative disorder. Most

animal models of PD developed to date are toxic, rather than genetic, models. Toxic

(also known as pharmacological) models—in particular, neurotoxin-based models—

were most effective in reproducing dopaminergic neuron death and striatal dopamine

deficit in non-human primates and rodents. The additional advantage of toxic models

is the feasibility of their use in non-human primates, whose motor symptoms and

neuronal structure are very similar to those of a human. The only limitation is the lack

of formation of classic Lewy bodies in primate brains.

Genetic models of PD are very limited. The reason for this limited availability is the

low contribution of the genetic component to this disorder: only 5% to 10% of all

PD cases are inherited. The most common mutations in this form of PD are those

in LRRK2 (which encodes an enzyme that may be involved in the deregulation of

α-synuclein phosphorylation), 256 PINK1 (PTEN-induced putative kinase 1), and

Parkin (which participates in the ubiquitin proteasome system). Transgenic mice with

knockouts in one of these genes exhibit only part of the PD phenotype, such as

motility abnormalities, mitochondrial and nigrostriatal neurotransmission deficits,

and others.257

255. Ribeiro F. M., Camargos E. R., Souza L. C. and Teixeira A. L. (2013) Animal models of neurodegenerative diseases. Rev Bras Psiqui-atr 35 Suppl 2: S82-91

256. Scholz S. W., Mhyre T., Ressom H., Shah S. and Federoff H. J. (2012) Genomics and bioin-formatics of Parkinson's disease. Cold Spring Harb Perspect Med 2: a009449

257. Blandini F. and Armentero M. T. (2012) Animal models of Parkinson's disease. FEBS J 279: 1156-1166


None of the single-gene transgenic mouse models featured substantial nigrostriatal

degeneration.258 Multi-gene transgenic mouse models are also available (with

α-synuclein and parkin or DJ-1 knockouts, or simultaneous silencing of PINK-1,

DJ-1, and parkin), but they too have only limited relevance to the symptomatic and

phenotypical spectrum of PD.259

Recently developed rat models with monogenic PD mutations are believed to be

advantageous over mouse models. Rat neuronal circuitry is closer to that of a

human, and they are less prone to anxiety than mice, which is important for the

evaluation of behavioral patterns. Transgenic rats with mutated α-synuclein do not

have major motor deficits, but do exhibit significant olfactory deficits.260 Rats with a

neuron-specific mutation in LRRK2, driven by adenoviral vectors, exhibit progressive

degeneration of nigral dopaminergic neurons.261,262

258. Chesselet M. F. (2008) In vivo alpha-synuclein overexpression in rodents: a useful model of Parkinson's disease? Exp Neurol 209: 22-27

259. Blandini F. and Armentero M. T. (2012) Animal models of Parkinson's disease. FEBS J 279: 1156-1166

260. Lelan F., Boyer C., Thinard R., Remy S., Usal C., et al. (2011) Effects of Human Alpha-Synu-clein A53T-A30P Mutations on SVZ and Local Olfactory Bulb Cell Proliferation in a Transgenic Rat Model of Parkinson Disease. Parkinsons Dis 2011: 987084

261. Zhou H., Huang C., Tong J., Hong W. C., Liu Y. J., et al. (2011) Temporal expression of mutant LRRK2 in adult rats impairs dopamine reuptake. Int J Biol Sci 7: 753-761

262. Dusonchet J., Kochubey O., Stafa K., Young S. M., Jr., Zufferey R., et al. (2011) A rat model of progressive nigral neurodegeneration in-duced by the Parkinson's disease-associated G2019S mutation in LRRK2. J Neurosci 31: 907-912


ReviewsBanerjee S., Riordan M. and Bhat M. A. (2014) Genetic aspects of autism spectrum disorders: insights from animal models. Front Cell Neurosci 8: 58

Katsnelson A. (2014) Drug development: The modelling challenge. Nature 508: S8-9



Jiang Y. H. and Ehlers M. D. (2013) Modeling autism by SHANK gene mutations in mice. Neuron 78: 8-27

Ng E., McGirr A., Wong A. H. and Roder J. C. (2013) Using rodents to model schizophrenia and substance use comorbidity. Neurosci Biobehav Rev 37: 896-910

Ribeiro F. M., Camargos E. R., Souza L. C. and Teixeira A. L. (2013) Animal models of neurodegenerative diseases. Rev Bras Psiquiatr 35 Suppl 2: S82-91


Blandini F. and Armentero M. T. (2012) Animal models of Parkinson's disease. FEBS J 279: 1156-1166

Crabtree D. M. and Zhang J. (2012) Genetically engineered mouse models of Parkinson's disease. Brain Res Bull 88: 13-32

Kvajo M., McKellar H. and Gogos J. A. (2012) Avoiding mouse traps in schizophrenia genetics: lessons and promises from current and emerging mouse models. Neuroscience 211: 136-164

Nomura J. and Takumi T. (2012) Animal models of psychiatric disorders that reflect human copy number variation. Neural Plast 2012: 589524

Pratt J., Winchester C., Dawson N. and Morris B. (2012) Advancing schizophrenia drug discovery: optimizing rodent models to bridge the translational gap. Nat Rev Drug Discov 11: 560-579


STEM-CELL MODELS

One of the most promising models for studying neurological diseases is patient-

derived iPSCs. These cells would allow testing of new therapeutic approaches

directly on human neural tissue that contains molecular alterations typical for ASD.263

The use of iPSCs in animal models has already provided a tool to correct abnormal

synaptic morphology and physiology, and to reverse behavioral alterations, even in

symptomatic animals.264

Human iPSC have been used as a model for studying PD.265 Because only a limited

population of dopaminergic neurons located in the midbrain is most prone to

degeneration, the engrafted stem cells must be matched to those affected

by degeneration.266

Another stem-cell model has been used for the treatment of ASD in clinical trials—

mesenchymal stem cells (MSCs).267 Reportedly, they overturn ASD symptoms by

restoring integration into the neural network, facilitating the recovery of synaptic

plasticity, and releasing anti-inflammatory cytokines.268

263. Ghosh A., Michalon A., Lindemann L., Fontou-ra P. and Santarelli L. (2013) Drug discovery for autism spectrum disorder: challenges and opportunities. Nat Rev Drug Discov 12: 777-790

264. Marchetto M. C., Carromeu C., Acab A., Yu D., Yeo G. W., et al. (2010) A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. Cell 143: 527-539

265. Sandoe J. and Eggan K. (2013) Opportunities and challenges of pluripotent stem cell neu-rodegenerative disease models. Nat Neurosci 16: 780-789

266. Sandoe J. and Eggan K. (2013) Opportunities and challenges of pluripotent stem cell neu-rodegenerative disease models. Nat Neurosci 16: 780-789

267. Siniscalco D., Bradstreet J. J., Sych N. and Antonucci N. (2014) Mesenchymal stem cells in treating autism: Novel insights. World J Stem Cells 6: 173-178

268. Siniscalco D., Bradstreet J. J., Sych N. and Antonucci N. (2014) Mesenchymal stem cells in treating autism: Novel insights. World J Stem Cells 6: 173-178


ReviewsGhosh A., Michalon A., Lindemann L., Fontoura P. and Santarelli L. (2013) Drug discovery for autism spectrum disorder: challenges and opportunities. Nat Rev Drug Discov 12: 777-790


Jung Y. W., Hysolli E., Kim K. Y., Tanaka Y. and Park I. H. (2012) Human induced pluripotent stem cells and neurodegenerative disease: prospects for novel therapies. Curr Opin Neurol 25: 125-130

ReferencesYoon K. J., Nguyen H. N., Ursini G., Zhang F., Kim N. S., et al. (2014) Modeling a genetic risk for schizophrenia in iPSCs and mice reveals neural stem cell deficits associated with adherens junctions and polarity. Cell Stem Cell 15: 79-91Defects in brain development can contribute to the onset of neuropsychiatric disorders. This study set out to identify the functional role of the 15q11.2 deletion on neural development using induced pluripotent stem cell (iPSC)-derived human neural precursor cells (hNPCs) by RNA-Seq and SNP-genotyping arrays. They found that haploinsufficiency of CYFIP1, a gene within 15q11.2, affects radial glial cells, leading to their ectopic localization outside of the ventricular zone.

Illumina Technology: HumanOmni2.5S

Ryan S. D., Dolatabadi N., Chan S. F., Zhang X., Akhtar M. W., et al. (2013) Isogenic human iPSC Parkinson's model shows nitrosative stress-induced dysfunction in MEF2-PGC1alpha transcription. Cell 155: 1351-1364An association has been reported between PD and exposure to mitochondrial toxins. In this study, a stem cell model was used to characterize the response to toxins using Illumina BeadArray for gene expression analysis. The authors identified a pathway whereby basal and toxin-induced nitrosative/oxidative stress results in S-nitrosylation of transcription factor MEF2C. They reported the alteration contributing to mitochondrial dysfunction and apoptotic cell death, indicating a mechanism and potential therapeutic target for PD.


Zhang Y., Schulz V. P., Reed B. D., Wang Z., Pan X., et al. (2013) Functional genomic screen of human stem cell differentiation reveals pathways involved in neurodevelopment and neurodegeneration. Proc Natl Acad Sci U S A 110: 12361-12366


BIOLOGY

Neurological and neurodegenerative diseases occur and develop as a result of

genetic and epigenetic mutations. However, many of these mutations are somatic

(non-inherited) and occur under the influence of biological factors, such as immune

activity, gut microbiome activity, and environmental factors. The contribution of

these factors at a multi-cell and single-cell level has long remained hypothetical and

controversial. However, the advent of high-resolution sequencing techniques now

allows the uncovering of these mechanisms and the significance of their contribution

to disease.

Immunity

The immune system, which was long believed to be independent from the central

nervous system (CNS), is now acknowledged to have an important contribution to

normal CNS function as well as to multiple neurological disorders.269,270 For example,

in PD, elevated levels of inflammatory cytokines are associated with more severe

forms of the disease, such as PD with dementia.271 In AD, an analysis of the two

largest GWAS has shown a significant overlap between disease-associated genes in

pathways associated with AD, cholesterol metabolism, and immune response.272 In

schizophrenia, a number of immune genes have been identified as genetic risk

factors associated with this disease.273,274,275 In autism, ongoing inflammation has

been determined as one of the common components of the disease. Interestingly,

fever in some autistic children was associated with improvement of their social

behavior, underpinning the involvement of inflammation in the symptomatic profile.276

“Both inflammation and oxidative stress tend to increase with age and are associated with a variety of chronic diseases. They have also been linked to neurodegeneration and proposed as factors that might contribute to schizophrenia.” Anthes 2014

269. Lindqvist D., Hall S., Surova Y., Nielsen H. M., Janelidze S., et al. (2013) Cerebrospinal fluid inflammatory markers in Parkinson's disease-associations with depression, fatigue, and cognitive impairment. Brain Behav Immun 33: 183-189Lynch M. A. and Mills K. H. (2012) Immunology meets neuroscience--opportuni-ties for immune intervention in neurodegenera-tive diseases. Brain Behav Immun 26: 1-10

270. Girgis R. R., Kumar S. S. and Brown A. S. (2014) The cytokine model of schizophrenia: emerging therapeutic strategies. Biol Psychia-try 75: 292-299

271. Lindqvist D., Hall S., Surova Y., Nielsen H. M., Janelidze S., et al. (2013) Cerebrospinal fluid inflammatory markers in Parkinson's disease--associations with depression, fatigue, and cognitive impairment. Brain Behav Immun 33: 183-189

272. Jones L., Harold D. and Williams J. (2010) Genetic evidence for the involvement of lipid metabolism in Alzheimer's disease. Biochim Biophys Acta 1801: 754-761

273. Stefansson H., Meyer-Lindenberg A., Steinberg S., Magnusdottir B., Morgen K., et al. (2014) CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 505: 361-366

274. Sinkus M. L., Adams C. E., Logel J., Freed-man R. and Leonard S. (2013) Expression of immune genes on chromosome 6p21.3-22.1 in schizophrenia. Brain Behav Immun 32: 51-62

275. Meyer U. (2011) Anti-inflammatory signaling in schizophrenia. Brain Behav Immun 25: 1507-1518

276. Curran L. K., Newschaffer C. J., Lee L. C., Crawford S. O., Johnston M. V., et al. (2007) Behaviors associated with fever in children with autism spectrum disorders. Pediatrics 120: e1386-1392


The blood-brain barrier prevents penetration of many types of immune cells into the

brain; however, a very limited number of certain immune cell populations, such as

dendritic cells and microglia, reside in the brain and facilitate the removal of dead

neurons.277,278 Neurodegeneration is most often accompanied by the accumulation of

microglia and monocytes around amyloid plaques and dying neurons.279 According

to one hypothesis of PD origin, the death of dopaminergic neurons in PD per se may

be facilitated by neuroinflammation.280 Infection of neurons and neighboring glial cells

with viruses, such as Japanese encephalitis virus (JIV), can increase the vulnerability

of neurons to factors such as aging, oxidative stress, environmental stress, and

genetic predisposition.281 Furthermore, neurons express some molecules normally

attributed to the immune system, thus uncovering an intricate interplay between

neuronal and immune systems.282

Immunotherapy is considered one of the promising approaches to treating

neurological diseases. Examples of such treatment include celecoxib, an inhibitor of

cyclooxygenase-2, which has shown some improvement of schizophrenia symptoms

in four studies283, and anti-ß-amyloid antibody, used in multiple trials up to phase III as

an agent to remove pathogenic ß-amyloid plaques.284,285

ReviewsGirgis R. R., Kumar S. S. and Brown A. S. (2014) The cytokine model of schizophrenia: emerging therapeutic strategies. Biol Psychiatry 75: 292-299


Lynch M. A. and Mills K. H. (2012) Immunology meets neuroscience--opportunities for immune intervention in neurodegenerative diseases. Brain Behav Immun 26: 1-10

Onore C., Careaga M. and Ashwood P. (2012) The role of immune dysfunction in the pathophysiology of autism. Brain Behav Immun 26: 383-392

Meyer U. (2011) Anti-inflammatory signaling in schizophrenia. Brain Behav Immun 25: 1507-1518

ReferencesSchizophrenia Working Group of the Psychiatric Genomics C. (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511: 421-427Schizophrenia is a highly heritable disorder, but the heritability is not found in a single gene effect. In this largest GWAS for schizophrenia to date, the authors used SNP arrays for 36,989 cases and 113,075 controls to determine genetic risk factors for the disorder. The authors found that the significant genetic associations were not randomly spread across the genome, but enriched among genes expressed in brain and genes that have been associated with typical co-morbidity diagnoses, such as ASD and intellectual disability. Interestingly, links were also enriched within genes related to immunity, which fits the existing hypothesis of immune dysregulation in schizophrenia.

Illumina Technology: Human1M, HumanOmni2.5, HumanOmniExpress, HumanHap550, Human610, HumanHap650Y, HumanHap300


278. Meyer U. (2011) Anti-inflammatory signaling in schizophrenia. Brain Behav Immun 25: 1507-1518


280. Lindqvist D., Hall S., Surova Y., Nielsen H. M., Janelidze S., et al. (2013) Cerebrospinal fluid inflammatory markers in Parkinson's disease--associations with depression, fatigue, and cognitive impairment. Brain Behav Immun 33: 183-189

281. Deleidi M. and Isacson O. (2012) Viral and inflammatory triggers of neurodegenerative diseases. Sci Transl Med 4: 121ps123

282. Boulanger L. M. (2009) Immune proteins in brain development and synaptic plasticity. Neuron 64: 93-109



285. www.clinicaltrials.gov


METAGENOMICS

Metagenomics refers to the study of genomic DNA obtained from multiple micro-

organisms that often cannot be cultured in the laboratory. Humans carry 10 times

more bacterial cells than human cells, and 100 times more bacterial genes than the

human genome.286 The new generation of sequencing technology, with its ability to

sequence thousands of organisms in parallel, has proved to be uniquely suited to this

application. Recent technical improvements allow nearly complete genome assembly

from individual microbes directly from environmental samples or clinical specimens,

without the need for cultivation methods.287 This accumulation of sequence

information has greatly expanded the appreciation of the dynamic nature of microbial

populations, as well as their impact on the environment and human health. With this

extraordinary and powerful set of sequencing tools now available, it is no surprise

that metagenomics has become one of the fastest-growing scientific disciplines.

The concept of a gut-brain axis has been developed recently, to highlight the

important influence of the metagenome on mental processes. It has been shown

that the gut microbiome plays an unexpectedly important role in the development

of depression, anxiety, irritable bowel syndrome, and neurological diseases, such

as autism289,290 and schizophrenia.291 The gut-microbial products may impose their

effect on the brain through chromatin plasticity, which causes changes in neuronal

transcription.292 Hsiao et al. proposed the use of microbiome-mediated therapies for

treating neurodevelopmental disorders.293

Interestingly, some gut bacteria (defined as psychobiotics) have a positive effect

on mental health and foster brain activity.294 These living organisms produce such

neuroactive substances as g-aminobutyric acid and serotonin, and are beneficial not

only for healthy individuals, but also for those with psychiatric disorders.295

Reviews Malkki H. (2014) Neurodevelopmental disorders: human gut microbiota alleviate behavioural symptoms in a mouse model of autism spectrum disorder. Nat Rev Neurol 10: 60

Stilling R. M., Dinan T. G. and Cryan J. F. (2014) Microbial genes, brain & behaviour - epigenetic regulation of the gut-brain axis. Genes Brain Behav 13: 69-86

Dinan T. G., Stanton C. and Cryan J. F. (2013) Psychobiotics: a novel class of psychotropic. Biol Psychiatry 74: 720-726

Lamendella R., VerBerkmoes N. and Jansson J. K. (2012) 'Omics' of the mammalian gut--new insights into function. Curr Opin Biotechnol 23: 491-500

Moon C. and Stappenbeck T. S. (2012) Viral interactions with the host and microbiota in the intestine. Curr Opin Immunol 24: 405-410

Nicholson J. K., Holmes E., Kinross J., Burcelin R., Gibson G., et al. (2012) Host-gut microbiota metabolic interactions. Science 336: 1262-1267

Reyes A., Semenkovich N. P., Whiteson K., Rohwer F. and Gordon J. I. (2012) Going viral: next-generation sequencing applied to phage populations in the human gut. Nat Rev Microbiol 10: 607-617

Zarowiecki M. (2012) Metagenomics with guts. Nat Rev Microbiol 10: 674

286. Barwick B. G., Abramovitz M., Kodani M., Moreno C. S., Nam R., et al. (2010) Prostate cancer genes associated with TMPRSS2-ERG gene fusion and prognostic of biochemical recurrence in multiple cohorts. Br J Cancer 102: 570-576

287. Lasken R. S. (2013) Single-cell sequencing in its prime. Nat Biotechnol 31: 211-212

288. Stilling R. M., Dinan T. G. and Cryan J. F. (2014) Microbial genes, brain & behaviour - epigenetic regulation of the gut-brain axis. Genes Brain Behav 13: 69-86

289. Hsiao E. Y., McBride S. W., Hsien S., Sharon G., Hyde E. R., et al. (2013) Microbiota modulate behavioral and physiological abnor-malities associated with neurodevelopmental disorders. Cell 155: 1451-1463

290. Malkki H. (2014) Neurodevelopmental disorders: human gut microbiota alleviate behavioural symptoms in a mouse model of autism spectrum disorder. Nat Rev Neurol 10: 60


292. Stilling R. M., Dinan T. G. and Cryan J. F. (2014) Microbial genes, brain & behaviour - epigenetic regulation of the gut-brain axis. Genes Brain Behav 13: 69-86

293. Hsiao E. Y., McBride S. W., Hsien S., Sharon G., Hyde E. R., et al. (2013) Microbiota modulate behavioral and physiological abnor-malities associated with neurodevelopmental disorders. Cell 155: 1451-1463

294. Dinan T. G., Stanton C. and Cryan J. F. (2013) Psychobiotics: a novel class of psychotropic. Biol Psychiatry 74: 720-726

295. Dinan T. G., Stanton C. and Cryan J. F. (2013) Psychobiotics: a novel class of psychotropic. Biol Psychiatry 74: 720-726


ReferencesMarkle J. G., Frank D. N., Mortin-Toth S., Robertson C. E., Feazel L. M., et al. (2013) Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339: 1084-1088Microbial exposure and sex hormones exert potent effects on autoimmune diseases. This study examined the effect of early-life microbial exposure on sex hormone levels and autoimmune disease in a non-obese diabetic (NOD) mouse model. The microbiome was characterized using 16S rRNA Illumina sequencing. Comparing the effects across both male and female mice, the results indicate that alteration of the gut microbiome in early life potently suppresses autoimmunity in animals at high genetic risk for disease.

Illumina Technology: MiSeq

Maurice C. F., Haiser H. J. and Turnbaugh P. J. (2013) Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 152: 39-50The microorganisms in the human gut microbiome are known to impact digestive health, but their influence on health by the metabolism of xenobiotics, including antibiotics and drugs, is still unclear. In this metagenomics study, xenobiotic-responsive genes were found across multiple bacterial phyla involved in various metabolic and stress-response pathways. The results suggest that xenobiotics may have important implications for the human gut microbiome.

Illumina Technology: HiSeq to sequence the V4 region of the 16S rRNA gene

Schloissnig S., Arumugam M., Sunagawa S., Mitreva M., Tap J., et al. (2013) Genomic variation landscape of the human gut microbiome. Nature 493: 45-50Subjects sampled at varying time intervals exhibited individuality and temporal stability of SNV patterns, despite considerable composition changes of their gut microbiota. This observation indicates that individual-specific strains are not easily replaced, and that an individual might have a unique metagenomic genotype, which may be exploitable for personalized diet or drug intake.

Illumina Technology: Illumina reads obtained from European MetaHIT study296 and U.S. Human Microbiome Project297

296. Qin J., Li R., Raes J., Arumugam M., Burgdorf K. S., et al. (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464: 59-65

297. Group N. H. W., Peterson J., Garges S., Giovanni M., McInnes P., et al. (2009) The NIH Human Microbiome Project. Genome Res 19: 2317-2323


ENVIRONMENTAL FACTORS

Exposure to microbes298,299, as well as various industrial and agricultural chemicals

(especially pesticides), has been implicated as conferring risk for multiple

diseases, including neurodegenerative disorders . Environmental pesticides serve

as mitochondrial toxins and induce nitrosative stress that inhibits activity of the

myocyte-specific enhancer factor 2C (MEF2C). This factor is involved in cardiac

morphogenesis, myogenesis, and vascular development. MEF2C, in turn, inhibits the

expression of peroxisome proliferator-activated receptor-gamma coactivator 1 alpha

(PGC-1 α), and hence suppresses the neuroprotective function of this transcriptional

co-activator.302

Neurodegenerative disorders, including PD, often co-exist with metabolic diseases.

For example, type 2 diabetes may stimulate the development of PD.303 Sekiyama et

al. suggest that genomic studies should contribute to better understanding of the

mechanism of this interaction, and help to develop strategies for new

therapeutic approaches.304

ReviewsSekiyama K., Takamatsu Y., Waragai M. and Hashimoto M. (2014) Role of genomics in translational research for Parkinson's disease. Biochem Biophys Res Commun 452: 226-235

Chung S. J., Armasu S. M., Anderson K. J., Biernacka J. M., Lesnick T. G., et al. (2013) Genetic susceptibility loci, environmental exposures, and Parkinson's disease: a case-control study of gene-environment interactions. Parkinsonism Relat Disord 19: 595-599

Sun Y., Christensen J. and Olsen J. (2013) Childhood epilepsy and maternal antibodies to microbial and tissue antigens during pregnancy. Epilepsy Res 107: 61-74

Bakulski K. M., Rozek L. S., Dolinoy D. C., Paulson H. L. and Hu H. (2012) Alzheimer's disease and environmental exposure to lead: the epidemiologic evidence and potential role of epigenetics. Curr Alzheimer Res 9: 563-573

Brown A. S. and Derkits E. J. (2010) Prenatal infection and schizophrenia: a review of epidemiologic and translational studies. Am J Psychiatry 167: 261-280

298. Brown A. S. and Derkits E. J. (2010) Prenatal infection and schizophrenia: a review of epidemiologic and translational studies. Am J Psychiatry 167: 261-280

299. Torrey E. F. and Yolken R. H. (2003) Toxoplas-ma gondii and schizophrenia. Emerg Infect Dis 9: 1375-1380

300. Jones B. C., Miller D. B., O'Callaghan J. P., Lu L., Unger E. L., et al. (2013) Systems analysis of genetic variation in MPTP neurotoxicity in mice. Neurotoxicology 37: 26-34


302. Ryan S. D., Dolatabadi N., Chan S. F., Zhang X., Akhtar M. W., et al. (2013) Isogenic human iPSC Parkinson's model shows nitrosative stress-induced dysfunction in MEF2-PGC1al-pha transcription. Cell 155: 1351-1364




SINGLE CELLS

Each cell type has a distinct lineage and function, which contributes to the functioning

of the tissue, the organ, and—ultimately—the organism. The lineage and developmental

stage of each cell determine how they respond to each other and to their environment.

While the ultimate goal of an exhaustive understanding of tissues at their cellular level is

still elusive, recent progress in single-cell analysis is offering a glimpse at the future.

A recent study of single cells and neurons in brain tissue found that most (≥ 95%)

neurons in normal brain tissue are euploid. However, a patient with hemimegalencephaly

(HMG) due to a somatic CNV of chromosome 1q had unexpected tetrasomy 1q in

20% of neurons. This observation suggests that CNVs in a minority of cells can cause

widespread brain dysfunction.305 This complexity can only be resolved with single-cell

sequencing approaches.

Recent advances in research have highlighted the mosaic genomes of individual neurons,

exhibiting CNVs even among cells that make up a specific region of the brain.306 Even

though genetic variations in the brain arise during fetal development,307 the functional

relevance of this mosaicism is unclear at present. It will be of interest not only to discover

the significance of mosaicism in the normal brain, but also to study its role in neurological

diseases and psychological disorders.308-311

Research has just begun to shed light mosaicism, where heterogeneity among cells is

notable at the genome level. If mosaicism exists in the genetic code among single cells,312

there are likely also variations in protein expression, epigenetic changes,313 and RNA

isoforms.314 Sequencing single cells provides a larger integrated image of the collected

data, helping to account for the effects of mosaicism on individual cellular phenotypes

within a given region of the brain.315,316,317

The high accuracy and specificity of NGS lends itself well to single-cell and low-level

DNA/RNA sequencing. The growing armamentarium of published techniques includes

the detection of DNA mutations, CNVs, DNA-protein binding, RNA splicing, and the

measurement of RNA expression values.

ReviewsChi K. R. (2014) Singled out for sequencing. Nat Methods 11: 13-17

Macaulay I. C. and Voet T. (2014) Single cell genomics: advances and future perspectives. PLoS Genet 10: e1004126

Weaver W. M., Tseng P., Kunze A., Masaeli M., Chung A. J., et al. (2014) Advances in high-throughput single-cell microtechnologies. Curr Opin Biotechnol 25: 114-123

Poduri A., Evrony G. D., Cai X. and Walsh C. A. (2013) Somatic mutation, genomic variation, and neurological disease. Science 341: 1237758

305. Cai X., Evrony G. D., Lehmann H. S., Elhosary P. C., Mehta B. K., et al. (2014) Single-Cell, Genome-wide Sequencing Identifies Clonal Somatic Copy-Number Variation in the Human Brain. Cell Rep 8: 1280-1289

306. McConnell M. J., Lindberg M. R., Brennand K. J., Piper J. C., Voet T., et al. (2013) Mosaic copy number variation in human neurons. Science 342: 632-637

307. Iourov I. Y., Vorsanova S. G. and Yurov Y. B. (2012) Single cell genomics of the brain: focus on neuronal diversity and neuropsychiatric diseases. Curr Genomics 13: 477-488

308. Iourov I. Y., Vorsanova S. G. and Yurov Y. B. (2012) Single cell genomics of the brain: focus on neuronal diversity and neuropsychiatric diseases. Curr Genomics 13: 477-488

309. Evrony G. D., Cai X., Lee E., Hills L. B., Elhosary P. C., et al. (2012) Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell 151: 483-496

310. Poduri A., Evrony G. D., Cai X. and Walsh C. A. (2013) Somatic mutation, genomic variation, and neurological disease. Science 341: 12377

311. Eberwine J. and Bartfai T. (2011) Single cell transcriptomics of hypothalamic warm sensi-tive neurons that control core body tempera-ture and fever response Signaling asymmetry and an extension of chemical neuroanatomy. Pharmacol Ther 129: 241-259


313. Lister R., Mukamel E. A., Nery J. R., Urich M., Puddifoot C. A., et al. (2013) Global epigenom-ic reconfiguration during mammalian brain development. Science 341: 1237905

314. Macaulay I. C. and Voet T. (2014) Single cell genomics: advances and future perspectives. PLoS Genet 10: e1004126


316. Poduri A., Evrony G. D., Cai X. and Walsh C. A. (2013) Somatic mutation, genomic variation, and neurological disease. Science 341: 1237758

317. Eberwine J. and Bartfai T. (2011) Single cell transcriptomics of hypothalamic warm sensi-tive neurons that control core body tempera-ture and fever response Signaling asymmetry and an extension of chemical neuroanatomy. Pharmacol Ther 129: 241-259


ReferencesLovatt D., Ruble B. K., Lee J., Dueck H., Kim T. K., et al. (2014) Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat Methods 11: 190-196RNA sequencing methods that rely on RNA extracted from cell mixtures do not convey the individual variability in expression among cells of the same tissue. In this paper, the authors present a transcriptome in vivo analysis (TIVA), which is applicable to single-cell studies. In combination with Illumina sequencing technology, the authors capture and analyze the transcriptome variance across single neurons both in culture and in vivo. This method is furthermore non-invasive and may be applied to intact tissue. It will enable detailed studies of cell heterogeneity in complex tissues that have been intractable previously, and it opens up the possibility of use in conjunction with in vivo live functional imaging.

Illumina Technology: 670k BeadChip Array

Young G. T., Gutteridge A., Fox H. D., Wilbrey A. L., Cao L., et al. (2014) Characterizing Human Stem Cell-derived Sensory Neurons at the Single-cell Level Reveals Their Ion Channel Expression and Utility in Pain Research. Mol Ther 22: 1530-1543Research into the biology of pain is commonly performed on animal models due to the lack of sensory neuronal cell lines. In this paper, the authors present human stem-cell derived sensory neurons and use a combination of population and single-cell techniques to perform detailed molecular, electrophysiological, and pharmacological phenotyping. The directed differentiation was monitored over 6 weeks and the gene expression characterized using Illumina BeadArrays. The authors show the derived neurons are both molecularly and functionally comparable to human sensory neurons derived from mature dorsal root ganglia.

Illumina Technology: BeadArrays

Lister R., Mukamel E. A., Nery J. R., Urich M., Puddifoot C. A., et al. (2013) Global epigenomic reconfiguration during mammalian brain development. Science 341: 1237905DNA methylation is implicated in mammalian brain development and plasticity underlying learning and memory. This paper reports the genome-wide composition, patterning, cell specificity and dynamics of DNA methylation at single-base resolution in human and mouse frontal cortex throughout their lifespan. The extensive methylome profiling was performed with ChIP-Seq on Illumina HiSeq, revealing methylation profiles at single-base resolution.

Illumina Technology: TruSeq RNA, TruSeq DNA, HiSeq

McConnell M. J., Lindberg M. R., Brennand K. J., Piper J. C., Voet T., et al. (2013) Mosaic copy number variation in human neurons. Science 342: 632-637The genome of a species varies not only between individuals, but also between mother and daughter cells as a result of errors and uneven distribution of DNA material after cell division. The extent of such genetic variation between somatic cells of the same individual has been hitherto unknown. With the advent of single-cell sequencing, it is now possible to examine variations within cells of the same tissue. In this study, the CNVs in individual neuron cells were studied using Illumina genome-wide sequencing. The authors discovered that CNVs are abundant even between neuronal cells from the same tissue.

Illumina Technology: Genome AnalyzerIIx, MiSeq, Nextera DNA Sample Prep Kit

Pan X., Durrett R. E., Zhu H., Tanaka Y., Li Y., et al. (2013) Two methods for full-length RNA sequencing for low quantities of cells and single cells. Proc Natl Acad Sci U S A 110: 594-599Gene expression profiling by RNA-Seq is a powerful tool for understanding the molecular activity of specific tissues. However, the heterogeneity of gene expression within a tissue requires RNA-Seq technology that can manage single-cell amounts of input RNA. This study presents two methods: Phi29 DNA polymerase–based mRNA transcriptome amplification (PMA), and semi–random-primed PCR-based mRNA transcriptome amplification (SMA). Both techniques are coupled with Illumina sequencing for expression detection from low RNA input amounts. Both protocols produced satisfactory detection/coverage of abundant mRNAs, even from a single cell.

Illumina Technology: HiSeq

Binder V., Bartenhagen C., Okpanyi V., Gombert M., Moehlendick B., et al. (2014) A New Workflow for Whole-Genome Sequencing of Single Human Cells. Hum Mutat 35: 1260-1270

Cai X., Evrony G. D., Lehmann H. S., Elhosary P. C., Mehta B. K., et al. (2014) Single-Cell, Genome-wide Sequencing Identifies Clonal Somatic Copy-Number Variation in the Human Brain. Cell Rep 8: 1280-1289


Abnet C. C., Wang Z., Song X., Hu N., Zhou F. Y., et al. (2012) Genotypic variants at 2q33 and risk of esophageal squamous cell carcinoma in China: a meta-analysis of genome-wide association studies. Hum Mol Genet 21: 2132-2141

Aguzzi A. and Rajendran L. (2009) The transcellular spread of cytosolic amyloids, prions, and prionoids. Neuron 64: 783-790

Akula N., Barb J., Jiang X., Wendland J. R., Choi K. H., et al. (2014) RNA-sequencing of the brain transcriptome implicates dysregulation of neuroplasticity, circadian rhythms and GTPase binding in bipolar disorder. Mol Psychiatry

Anthes E. (2014) Ageing: Live faster, die younger. Nature 508: S16-17

Baba M., Nakajo S., Tu P. H., Tomita T., Nakaya K., et al. (1998) Aggregation of α-synuclein in Lewy bodies of sporadic Parkinson's disease and dementia with Lewy bodies. Am J Pathol 152: 879-884

Bai B., Hales C. M., Chen P. C., Gozal Y., Dammer E. B., et al. (2013) U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease. Proc Natl Acad Sci U S A 110: 16562-16567

Bakulski K. M., Rozek L. S., Dolinoy D. C., Paulson H. L. and Hu H. (2012) Alzheimer's disease and environmental exposure to lead: the epidemiologic evidence and potential role of epigenetics. Curr Alzheimer Res 9: 563-573

Banerjee S., Riordan M. and Bhat M. A. (2014) Genetic aspects of autism spectrum disorders: insights from animal models. Front Cell Neurosci 8: 58

Barwick B. G., Abramovitz M., Kodani M., Moreno C. S., Nam R., et al. (2010) Prostate cancer genes associated with TMPRSS2-ERG gene fusion and prognostic of biochemical recurrence in multiple cohorts. Br J Cancer 102: 570-576

Basha M. R., Wei W., Bakheet S. A., Benitez N., Siddiqi H. K., et al. (2005) The fetal basis of amyloidogenesis: exposure to lead and latent overexpression of amyloid precursor protein and beta-amyloid in the aging brain. J Neurosci 25: 823-829

Beri S., Tonna N., Menozzi G., Bonaglia M. C., Sala C., et al. (2007) DNA methylation regulates tissue-specific expression of Shank3. J Neurochem 101: 1380-1391

Bertram L., Lill C. M. and Tanzi R. E. (2010) The genetics of Alzheimer disease: back to the future. Neuron 68: 270-281

Bien-Ly N., Andrews-Zwilling Y., Xu Q., Bernardo A., Wang C., et al. (2011) C-terminal-truncated apolipoprotein (apo) E4 inefficiently clears amyloid-beta (Abeta) and acts in concert with Abeta to elicit neuronal and behavioral deficits in mice. Proc Natl Acad Sci U S A 108: 4236-4241

Binder V., Bartenhagen C., Okpanyi V., Gombert M., Moehlendick B., et al. (2014) A New Workflow for Whole-Genome Sequencing of Single Human Cells. Hum Mutat 35: 1260-1270

Blandini F. and Armentero M. T. (2012) Animal models of Parkinson's disease. FEBS J 279: 1156-1166

Blurton-Jones M. and Laferla F. M. (2006) Pathways by which Abeta facilitates tau pathology. Curr Alzheimer Res 3: 437-448

Booij B. B., Lindahl T., Wetterberg P., Skaane N. V., Saebo S., et al. (2011) A gene expression pattern in blood for the early detection of Alzheimer's disease. J Alzheimers Dis 23: 109-119

Borglum A. D., Demontis D., Grove J., Pallesen J., Hollegaard M. V., et al. (2014) Genome-wide study of association and interaction with maternal cytomegalovirus infection suggests new schizophrenia loci. Mol Psychiatry 19: 325-333

Boulanger L. M. (2009) Immune proteins in brain development and synaptic plasticity. Neuron 64: 93-109


Bras J., Guerreiro R., Darwent L., Parkkinen L., Ansorge O., et al. (2014) Genetic analysis implicates APOE, SNCA and suggests lysosomal dysfunction in the etiology of dementia with Lewy bodies. Hum Mol Genet. In Press

Brody H. (2014) Schizophrenia. Nature 508: S1

Brookmeyer R., Johnson E., Ziegler-Graham K. and Arrighi H. M. (2007) Forecasting the global burden of Alzheimer's disease. Alzheimers Dement 3: 186-191

Brown A. S. and Derkits E. J. (2010) Prenatal infection and schizophrenia: a review of epidemiologic and translational studies. Am J Psychiatry 167: 261-280

Buxbaum J. (2013) The Neuroscience of Autism Spectrum Disorders.

Cai X., Evrony G. D., Lehmann H. S., Elhosary P. C., Mehta B. K., et al. (2014) Single-Cell, Genome-wide Sequencing Identifies Clonal Somatic Copy-Number Variation in the Human Brain. Cell Rep 8: 1280-1289

Carter H. B., Morrell C. H., Pearson J. D., Brant L. J., Plato C. C., et al. (1992) Estimation of prostatic growth using serial prostate-specific antigen measurements in men with and without prostate disease. Cancer Res 52: 3323-3328

Carter H. B., Pearson J. D., Metter E. J., Brant L. J., Chan D. W., et al. (1992) Longitudinal evaluation of prostate-specific antigen levels in men with and without prostate disease. JAMA 267: 2215-2220

Chapman J., Rees E., Harold D., Ivanov D., Gerrish A., et al. (2013) A genome-wide study shows a limited contribution of rare copy number variants to Alzheimer's disease risk. Hum Mol Genet 22: 816-824

Chen-Plotkin A. S., Hu W. T., Siderowf A., Weintraub D., Goldmann Gross R., et al. (2011) Plasma epidermal growth factor levels predict cognitive decline in Parkinson disease. Ann Neurol 69: 655-663

Chesselet M. F. (2008) In vivo alpha-synuclein overexpression in rodents: a useful model of Parkinson's disease? Exp Neurol 209: 22-27

Chi K. R. (2014) Singled out for sequencing. Nat Methods 11: 13-17

Ching T. T., Maunakea A. K., Jun P., Hong C., Zardo G., et al. (2005) Epigenome analyses using BAC microarrays identify evolutionary conservation of tissue-specific methylation of SHANK3. Nat Genet 37: 645-651

Chouliaras L., Rutten B. P., Kenis G., Peerbooms O., Visser P. J., et al. (2010) Epigenetic regulation in the pathophysiology of Alzheimer's disease. Prog Neurobiol 90: 498-510

Chung S. J., Armasu S. M., Anderson K. J., Biernacka J. M., Lesnick T. G., et al. (2013) Genetic susceptibility loci, environmental exposures, and Parkinson's disease: a case-control study of gene-environment interactions. Parkinsonism Relat Disord 19: 595-599

Clayton D. F. and George J. M. (1998) The synucleins: a family of proteins involved in synaptic function, plasticity, neurodegeneration and disease. Trends Neurosci 21: 249-254

Clayton D. F., Balakrishnan C. N. and London S. E. (2009) Integrating genomes, brain and behavior in the study of songbirds. Curr Biol 19: R865-873

Cookson M. R. (2005) The biochemistry of Parkinson's disease. Annu Rev Biochem 74: 29-52

Cooper A. A., Gitler A. D., Cashikar A., Haynes C. M., Hill K. J., et al. (2006) Alpha-synuclein blocks ER-Golgi traffic and Rab1 rescues neuron loss in Parkinson's models. Science 313: 324-328

BIBLIOGRAPHY


Coppede F. (2014) The potential of epigenetic therapies in neurodegenerative diseases. Front Genet 5: 220

Crabtree D. M. and Zhang J. (2012) Genetically engineered mouse models of Parkinson's disease. Brain Res Bull 88: 13-32

Cross-Disorder Group of the Psychiatric Genomics C., Lee S. H., Ripke S., Neale B. M., Faraone S. V., et al. (2013) Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet 45: 984-994

Cruchaga C., Kauwe J. S., Harari O., Jin S. C., Cai Y., et al. (2013) GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease. Neuron 78: 256-268

Cruchaga C., Karch C. M., Jin S. C., Benitez B. A., Cai Y., et al. (2014) Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer's disease. Nature 505: 550-554

Cukier H. N., Dueker N. D., Slifer S. H., Lee J. M., Whitehead P. L., et al. (2014) Exome sequencing of extended families with autism reveals genes shared across neurodevelopmental and neuropsychiatric disorders. Mol Autism 5: 1

Curran L. K., Newschaffer C. J., Lee L. C., Crawford S. O., Johnston M. V., et al. (2007) Behaviors associated with fever in children with autism spectrum disorders. Pediatrics 120: e1386-1392

De Jager P. L., Srivastava G., Lunnon K., Burgess J., Schalkwyk L. C., et al. (2014) Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 17: 1156-1163

de Lau L. M. and Breteler M. M. (2006) Epidemiology of Parkinson's disease. Lancet Neurol 5: 525-535

de Leon J. and Diaz F. J. (2005) A meta-analysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors. Schizophr Res 76: 135-157

De Strooper B. and Voet T. (2012) Alzheimer's disease: A protective mutation. Nature 488: 38-39

Deas E., Plun-Favreau H. and Wood N. W. (2009) PINK1 function in health and disease. EMBO Mol Med 1: 152-165

Deleidi M. and Isacson O. (2012) Viral and inflammatory triggers of neurodegenerative diseases. Sci Transl Med 4: 121ps123

Dinan T. G., Stanton C. and Cryan J. F. (2013) Psychobiotics: a novel class of psychotropic. Biol Psychiatry 74: 720-726

Ding H., Dolan P. J. and Johnson G. V. (2008) Histone deacetylase 6 interacts with the microtubule-associated protein tau. J Neurochem 106: 2119-2130

n L. (1999) Dual diagnosis of substance abuse in schizophrenia: prevalence and impact on outcomes. Schizophr Res 35 Suppl: S93-100

Dolgin E. (2014) Therapeutics: Negative feedback. Nature 508: S10-11

Durkin M. S., Maenner M. J., Newschaffer C. J., Lee L. C., Cunniff C. M., et al. (2008) Advanced parental age and the risk of autism spectrum disorder. Am J Epidemiol 168: 1268-1276

Dusonchet J., Kochubey O., Stafa K., Young S. M., Jr., Zufferey R., et al. (2011) A rat model of progressive nigral neurodegeneration induced by the Parkinson's disease-associated G2019S mutation in LRRK2. J Neurosci 31: 907-912


Eberwine J. and Bartfai T. (2011) Single cell transcriptomics of hypothalamic warm sensitive neurons that control core body temperature and fever response Signaling asymmetry and an extension of chemical neuroanatomy. Pharmacol Ther 129: 241-259

Eisenberg D. and Jucker M. (2012) The amyloid state of proteins in human diseases. Cell 148: 1188-1203

Elert E. (2014) Aetiology: Searching for schizophrenia's roots. Nature 508: S2-3

Emre M., Aarsland D., Brown R., Burn D. J., Duyckaerts C., et al. (2007) Clinical diagnostic criteria for dementia associated with Parkinson's disease. Mov Disord 22: 1689-1707; quiz 1837

Evrony G. D., Cai X., Lee E., Hills L. B., Elhosary P. C., et al. (2012) Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell 151: 483-496

Fan H. C., Chen S. J., Harn H. J. and Lin S. Z. (2013) Parkinson's disease: from genetics to treatments. Cell Transplant 22: 639-652

Fehlbaum-Beurdeley P., Sol O., Desire L., Touchon J., Dantoine T., et al. (2012) Validation of AclarusDx, a blood-based transcriptomic signature for the diagnosis of Alzheimer's disease. J Alzheimers Dis 32: 169-181

Fischer A., Sananbenesi F., Wang X., Dobbin M. and Tsai L. H. (2007) Recovery of learning and memory is associated with chromatin remodelling. Nature 447: 178-182

Fogel B. L., Cho E., Wahnich A., Gao F., Becherel O. J., et al. (2014) Mutation of senataxin alters disease-specific transcriptional networks in patients with ataxia with oculomotor apraxia type 2. Hum Mol Genet 23: 4758-4769

Forshew T., Murtaza M., Parkinson C., Gale D., Tsui D. W., et al. (2012) Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med 4: 136ra168

Fromer M., Pocklington A. J., Kavanagh D. H., Williams H. J., Dwyer S., et al. (2014) De novo mutations in schizophrenia implicate synaptic networks. Nature 506: 179-184


Gadow K. D. (2013) Association of schizophrenia spectrum and autism spectrum disorder (ASD) symptoms in children with ASD and clinic controls. Res Dev Disabil 34: 1289-1299

Ganat Y. M., Calder E. L., Kriks S., Nelander J., Tu E. Y., et al. (2012) Identification of embryonic stem cell-derived midbrain dopaminergic neurons for engraftment. J Clin Invest 122: 2928-2939

Gau S. S., Liao H. M., Hong C. C., Chien W. H. and Chen C. H. (2012) Identification of two inherited copy number variants in a male with autism supports two-hit and compound heterozygosity models of autism. Am J Med Genet B Neuropsychiatr Genet 159B: 710-717

Gaugler T., Klei L., Sanders S. J., Bodea C. A., Goldberg A. P., et al. (2014) Most genetic risk for autism resides with common variation. Nat Genet 46: 881-885

Gauthier J., Champagne N., Lafreniere R. G., Xiong L., Spiegelman D., et al. (2010) De novo mutations in the gene encoding the synaptic scaffolding protein SHANK3 in patients ascertained for schizophrenia. Proc Natl Acad Sci U S A 107: 7863-7868

Genin E., Hannequin D., Wallon D., Sleegers K., Hiltunen M., et al. (2011) APOE and Alzheimer disease: a major gene with semi-dominant inheritance. Mol Psychiatry 16: 903-907

Ghosh A., Michalon A., Lindemann L., Fontoura P. and Santarelli L. (2013) Drug discovery for autism spectrum disorder: challenges and opportunities. Nat Rev Drug Discov 12: 777-790

Gilbert J. A., Krajmalnik-Brown R., Porazinska D. L., Weiss S. J. and Knight R. (2013) Toward effective probiotics for autism and other neurodevelopmental disorders. Cell 155: 1446-1448

Gilissen C., Hehir-Kwa J. Y., Thung D. T., van de Vorst M., van Bon B. W., et al. (2014) Genome sequencing identifies major causes of severe intellectual disability. Nature 511: 344-347

Gilman S. R., Iossifov I., Levy D., Ronemus M., Wigler M., et al. (2011) Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70: 898-907

Girgis R. R., Kumar S. S. and Brown A. S. (2014) The cytokine model of schizophrenia: emerging therapeutic strategies. Biol Psychiatry 75: 292-299


Glessner J. T., Wang K., Cai G., Korvatska O., Kim C. E., et al. (2009) Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459: 569-573

Gomez-Herreros F., Schuurs-Hoeijmakers J. H., McCormack M., Greally M. T., Rulten S., et al. (2014) TDP2 protects transcription from abortive topoisomerase activity and is required for normal neural function. Nat Genet 46: 516-521

Graff J., Rei D., Guan J. S., Wang W. Y., Seo J., et al. (2012) An epigenetic blockade of cognitive functions in the neurodegenerating brain. Nature 483: 222-226

Group N. H. W., Peterson J., Garges S., Giovanni M., McInnes P., et al. (2009) The NIH Human Microbiome Project. Genome Res 19: 2317-2323

Guerreiro R. J., Gustafson D. R. and Hardy J. (2012) The genetic architecture of Alzheimer's disease: beyond APP, PSENs and APOE. Neurobiol Aging 33: 437-456

Guffanti G., Torri F., Rasmussen J., Clark A. P., Lakatos A., et al. (2013) Increased CNV-region deletions in mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects in the ADNI sample. Genomics 102: 112-122

Guilmatre A., Dubourg C., Mosca A. L., Legallic S., Goldenberg A., et al. (2009) Recurrent rearrangements in synaptic and neurodevelopmental genes and shared biologic pathways in schizophrenia, autism, and mental retardation. Arch Gen Psychiatry 66: 947-956

Guo J. L. and Lee V. M. (2014) Cell-to-cell transmission of pathogenic proteins in neurodegenerative diseases. Nat Med 20: 130-138

Hafner M., Landgraf P., Ludwig J., Rice A., Ojo T., et al. (2008) Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods 44: 3-12

Harold D., Abraham R., Hollingworth P., Sims R., Gerrish A., et al. (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease. Nat Genet 41: 1088-1093

Hollerhage M., Goebel J. N., de Andrade A., Hildebrandt T., Dolga A., et al. (2014) Trifluoperazine rescues human dopaminergic cells from wild-type alpha-synuclein-induced toxicity. Neurobiol Aging 35: 1700-1711

Hollingworth P., Harold D., Sims R., Gerrish A., Lambert J. C., et al. (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease. Nat Genet 43: 429-435


Hsiao E. Y., McBride S. W., Hsien S., Sharon G., Hyde E. R., et al. (2013) Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell 155: 1451-1463

Huang Y. (2010) Abeta-independent roles of apolipoprotein E4 in the pathogenesis of Alzheimer's disease. Trends Mol Med 16: 287-294

Huang Y. and Mucke L. (2012) Alzheimer mechanisms and therapeutic strategies. Cell 148: 1204-1222

Hyman S. E. (2014) Perspective: Revealing molecular secrets. Nature 508: S20

Iafrate A. J., Feuk L., Rivera M. N., Listewnik M. L., Donahoe P. K., et al. (2004) Detection of large-scale variation in the human genome. Nat Genet 36: 949-951

Ionita-Laza I., Xu B., Makarov V., Buxbaum J. D., Roos J. L., et al. (2014) Scan statistic-based analysis of exome sequencing data identifies FAN1 at 15q13.3 as a susceptibility gene for schizophrenia and autism. Proc Natl Acad Sci U S A 111: 343-348

Iourov I. Y., Vorsanova S. G. and Yurov Y. B. (2012) Single cell genomics of the brain: focus on neuronal diversity and neuropsychiatric diseases. Curr Genomics 13: 477-488

Irwin D. J., Lee V. M. and Trojanowski J. Q. (2013) Parkinson's disease dementia: convergence of alpha-synuclein, tau and amyloid-beta pathologies. Nat Rev Neurosci 14: 626-636

Jacquemont S., Coe B. P., Hersch M., Duyzend M. H., Krumm N., et al. (2014) A higher mutational burden in females supports a "female protective model" in neurodevelopmental disorders. Am J Hum Genet 94: 415-425

Jiang Y. H. and Ehlers M. D. (2013) Modeling autism by SHANK gene mutations in mice. Neuron 78: 8-27

Jiang Y. H., Yuen R. K., Jin X., Wang M., Chen N., et al. (2013) Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. Am J Hum Genet 93: 249-263

Jones B. C., Miller D. B., O'Callaghan J. P., Lu L., Unger E. L., et al. (2013) Systems analysis of genetic variation in MPTP neurotoxicity in mice. Neurotoxicology 37: 26-34

Jones L., Harold D. and Williams J. (2010) Genetic evidence for the involvement of lipid metabolism in Alzheimer's disease. Biochim Biophys Acta 1801: 754-761

Jonsson T., Atwal J. K., Steinberg S., Snaedal J., Jonsson P. V., et al. (2012) A mutation in APP protects against Alzheimer's disease and age-related cognitive decline. Nature 488: 96-99

Jowaed A., Schmitt I., Kaut O. and Wullner U. (2010) Methylation regulates alpha-synuclein expression and is decreased in Parkinson's disease patients' brains. J Neurosci 30: 6355-6359

Jucker M. (2010) The benefits and limitations of animal models for translational research in neurodegenerative diseases. Nat Med 16: 1210-1214

Jucker M. and Walker L. C. (2013) Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature 501: 45-51

Jung Y. W., Hysolli E., Kim K. Y., Tanaka Y. and Park I. H. (2012) Human induced pluripotent stem cells and neurodegenerative disease: prospects for novel therapies. Curr Opin Neurol 25: 125-130

Karayannis T., Au E., Patel J. C., Kruglikov I., Markx S., et al. (2014) Cntnap4 differentially contributes to GABAergic and dopaminergic synaptic transmission. Nature 511: 236-240

Karlsson M. K., Sharma P., Aasly J., Toft M., Skogar O., et al. (2013) Found in transcription: accurate Parkinson's disease classification in peripheral blood. J Parkinsons Dis 3: 19-29

Katsnelson A. (2014) Drug development: The modelling challenge. Nature 508: S8-9

Keogh M. J. and Chinnery P. F. (2013) Next generation sequencing for neurological diseases: new hope or new hype? Clin Neurol Neurosurg 115: 948-953

Kilgore M., Miller C. A., Fass D. M., Hennig K. M., Haggarty S. J., et al. (2010) Inhibitors of class 1 histone deacetylases reverse contextual memory deficits in a mouse model of Alzheimer's disease. Neuropsychopharmacology 35: 870-880

Kinde I., Bettegowda C., Wang Y., Wu J., Agrawal N., et al. (2013) Evaluation of DNA from the Papanicolaou test to detect ovarian and endometrial cancers. Sci Transl Med 5: 167ra164

King C. and Murphy G. H. (2014) A Systematic Review of People with Autism Spectrum Disorder and the Criminal Justice System. J Autism Dev Disord

King I. F., Yandava C. N., Mabb A. M., Hsiao J. S., Huang H. S., et al. (2013) Topoisomerases facilitate transcription of long genes linked to autism. Nature 501: 58-62

Kirik D., Rosenblad C., Burger C., Lundberg C., Johansen T. E., et al. (2002) Parkinson-like neurodegeneration induced by targeted overexpression of alpha-synuclein in the nigrostriatal system. J Neurosci 22: 2780-2791

Kirov G., Rees E., Walters J. T., Escott-Price V., Georgieva L., et al. (2014) The penetrance of copy number variations for schizophrenia and developmental delay. Biol Psychiatry 75: 378-385



Kong A., Frigge M. L., Masson G., Besenbacher S., Sulem P., et al. (2012) Rate of de novo mutations and the importance of father's age to disease risk. Nature 488: 471-475

Kriukiene E., Labrie V., Khare T., Urbanaviciute G., Lapinaite A., et al. (2013) DNA unmethylome profiling by covalent capture of CpG sites. Nat Commun 4: 2190

Kvajo M., McKellar H. and Gogos J. A. (2012) Avoiding mouse traps in schizophrenia genetics: lessons and promises from current and emerging mouse models. Neuroscience 211: 136-164

Ladd-Acosta C., Hansen K. D., Briem E., Fallin M. D., Kaufmann W. E., et al. (2013) Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry

Ladd-Acosta C., Hansen K. D., Briem E., Fallin M. D., Kaufmann W. E., et al. (2014) Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry 19: 862-871

Lambert J. C., Heath S., Even G., Campion D., Sleegers K., et al. (2009) Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease. Nat Genet 41: 1094-1099

Lambert J. C., Ibrahim-Verbaas C. A., Harold D., Naj A. C., Sims R., et al. (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet 45: 1452-1458

Lamendella R., VerBerkmoes N. and Jansson J. K. (2012) 'Omics' of the mammalian gut--new insights into function. Curr Opin Biotechnol 23: 491-500

Lasken R. S. (2013) Single-cell sequencing in its prime. Nat Biotechnol 31: 211-212

Lee H., Lin M. C., Kornblum H. I., Papazian D. M. and Nelson S. F. (2014) Exome sequencing identifies de novo gain of function missense mutation in KCND2 in identical twins with autism and seizures that slows potassium channel inactivation. Hum Mol Genet 23: 3481-3489

Lelan F., Boyer C., Thinard R., Remy S., Usal C., et al. (2011) Effects of Human Alpha-synuclein A53T-A30P Mutations on SVZ and Local Olfactory Bulb Cell Proliferation in a Transgenic Rat Model of Parkinson Disease. Parkinsons Dis 2011: 987084

Levy D., Ronemus M., Yamrom B., Lee Y. H., Leotta A., et al. (2011) Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70: 886-897

Lewis J., Dickson D. W., Lin W. L., Chisholm L., Corral A., et al. (2001) Enhanced neurofibrillary degeneration in transgenic mice expressing mutant tau and APP. Science 293: 1487-1491

Li M. M., Jiang T., Sun Z., Zhang Q., Tan C. C., et al. (2014) Genome-wide microRNA expression profiles in hippocampus of rats with chronic temporal lobe epilepsy. Sci Rep 4: 4734

Lindqvist D., Hall S., Surova Y., Nielsen H. M., Janelidze S., et al. (2013) Cerebrospinal fluid inflammatory markers in Parkinson's disease--associations with depression, fatigue, and cognitive impairment. Brain Behav Immun 33: 183-189


Lipska B. K. and Weinberger D. R. (2000) To model a psychiatric disorder in animals: schizophrenia as a reality test. Neuropsychopharmacology 23: 223-239

Lister R., Mukamel E. A., Nery J. R., Urich M., Puddifoot C. A., et al. (2013) Global epigenomic reconfiguration during mammalian brain development. Science 341: 1237905

Lo Y. M., Chan K. C., Sun H., Chen E. Z., Jiang P., et al. (2010) Maternal plasma DNA sequencing reveals the genome-wide genetic and mutational profile of the fetus. Sci Transl Med 2: 61ra91

Losh M., Childress D., Lam K. and Piven J. (2008) Defining key features of the broad autism phenotype: a comparison across parents of multiple- and single-incidence autism families. Am J Med Genet B Neuropsychiatr Genet 147B: 424-433

Lovatt D., Ruble B. K., Lee J., Dueck H., Kim T. K., et al. (2014) Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat Methods 11: 190-196

Lu H., Liu X., Deng Y. and Qing H. (2013) DNA methylation, a hand behind neurodegenerative diseases. Front Aging Neurosci 5: 85

Lunnon K., Smith R., Hannon E., De Jager P. L., Srivastava G., et al. (2014) Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease. Nat Neurosci 17: 1164-1170

Lynch M. A. and Mills K. H. (2012) Immunology meets neuroscience--opportunities for immune intervention in neurodegenerative diseases. Brain Behav Immun 26: 1-10

Lyons M. R. and West A. E. (2011) Mechanisms of specificity in neuronal activity-regulated gene transcription. Prog Neurobiol 94: 259-295

Macaulay I. C. and Voet T. (2014) Single cell genomics: advances and future perspectives. PLoS Genet 10: e1004126

Maciotta S., Meregalli M. and Torrente Y. (2013) The involvement of microRNAs in neurodegenerative diseases. Front Cell Neurosci 7: 265

Malhotra D., McCarthy S., Michaelson J. J., Vacic V., Burdick K. E., et al. (2011) High frequencies of de novo CNVs in bipolar disorder and schizophrenia. Neuron 72: 951-963


Malkki H. (2014) Neurodevelopmental disorders: human gut microbiota alleviate behavioural symptoms in a mouse model of autism spectrum disorder. Nat Rev Neurol 10: 60

Marchetto M. C., Carromeu C., Acab A., Yu D., Yeo G. W., et al. (2010) A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. Cell 143: 527-539

Markle J. G., Frank D. N., Mortin-Toth S., Robertson C. E., Feazel L. M., et al. (2013) Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339: 1084-1088

Martin C. L., Duvall J. A., Ilkin Y., Simon J. S., Arreaza M. G., et al. (2007) Cytogenetic and molecular characterization of A2BP1/FOX1 as a candidate gene for autism. Am J Med Genet B Neuropsychiatr Genet 144B: 869-876

Matsumoto L., Takuma H., Tamaoka A., Kurisaki H., Date H., et al. (2010) CpG demethylation enhances alpha-synuclein expression and affects the pathogenesis of Parkinson's disease. PLoS One 5: e15522

Maunakea A. K., Nagarajan R. P., Bilenky M., Ballinger T. J., D'Souza C., et al. (2010) Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466: 253-257

Maurice C. F., Haiser H. J. and Turnbaugh P. J. (2013) Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 152: 39-50

McCarroll S. A. and Hyman S. E. (2013) Progress in the genetics of polygenic brain disorders: significant new challenges for neurobiology. Neuron 80: 578-587


McConnell M. J., Lindberg M. R., Brennand K. J., Piper J. C., Voet T., et al. (2013) Mosaic copy number variation in human neurons. Science 342: 632-637

McGrath L. M., Yu D., Marshall C., Davis L. K., Thiruvahindrapuram B., et al. (2014) Copy number variation in obsessive-compulsive disorder and tourette syndrome: a cross-disorder study. J Am Acad Child Adolesc Psychiatry 53: 910-919


Medland S. E., Jahanshad N., Neale B. M. and Thompson P. M. (2014) Whole-genome analyses of whole-brain data: working within an expanded search space. Nat Neurosci 17: 791-800

Mencacci N. E., Isaias I. U., Reich M. M., Ganos C., Plagnol V., et al. (2014) Parkinson's disease in GTP cyclohydrolase 1 mutation carriers. Brain 137: 2480-2492

Meyer U. (2011) Anti-inflammatory signaling in schizophrenia. Brain Behav Immun 25: 1507-1518

Miles J. H. (2011) Autism spectrum disorders--a genetics review. Genet Med 13: 278-294


Mitsui J. and Tsuji S. (2014) Genomic Aspects of Sporadic Neurodegenerative Diseases. Biochem Biophys Res Commun 452: 221-225

Moon C. and Stappenbeck T. S. (2012) Viral interactions with the host and microbiota in the intestine. Curr Opin Immunol 24: 405-410

Morihara T., Hayashi N., Yokokoji M., Akatsu H., Silverman M. A., et al. (2014) Transcriptome analysis of distinct mouse strains reveals kinesin light chain-1 splicing as an amyloid-beta accumulation modifier. Proc Natl Acad Sci U S A 111: 2638-2643

Morris D. W., Pearson R. D., Cormican P., Kenny E. M., O'Dushlaine C. T., et al. (2014) An inherited duplication at the gene p21 Protein-Activated Kinase 7 (PAK7) is a risk factor for psychosis. Hum Mol Genet 23: 3316-3326

Mutez E., Nkiliza A., Belarbi K., de Broucker A., Vanbesien-Mailliot C., et al. (2014) Involvement of the immune system, endocytosis and EIF2 signaling in both genetically determined and sporadic forms of Parkinson's disease. Neurobiol Dis 63: 165-170

Naj A. C., Jun G., Beecham G. W., Wang L. S., Vardarajan B. N., et al. (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nat Genet 43: 436-441

Nalls M. A., Pankratz N., Lill C. M., Do C. B., Hernandez D. G., et al. (2014) Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease. Nat Genet 46: 989-993

Nalls M. A., Pankratz N., Lill C. M., Do C. B., Hernandez D. G., et al. (2014) Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease. Nat Genet 46: 989-993


Ng E., McGirr A., Wong A. H. and Roder J. C. (2013) Using rodents to model schizophrenia and substance use comorbidity. Neurosci Biobehav Rev 37: 896-910

Nicholson J. K., Holmes E., Kinross J., Burcelin R., Gibson G., et al. (2012) Host-gut microbiota metabolic interactions. Science 336: 1262-1267

Nomura J. and Takumi T. (2012) Animal models of psychiatric disorders that reflect human copy number variation. Neural Plast 2012: 589524

Norris A. D., Gao S., Norris M. L., Ray D., Ramani A. K., et al. (2014) A pair of RNA-binding proteins controls networks of splicing events contributing to specialization of neural cell types. Mol Cell 54: 946-959

Oddo S., Caccamo A., Shepherd J. D., Murphy M. P., Golde T. E., et al. (2003) Triple-transgenic model of Alzheimer's disease with plaques and tangles: intracellular Abeta and synaptic dysfunction. Neuron 39: 409-421

Okamoto N., Kubota T., Nakamura Y., Murakami R., Nishikubo T., et al. (2007) 22q13 Microduplication in two patients with common clinical manifestations: a recognizable syndrome? Am J Med Genet A 143A: 2804-2809

Oldmeadow C., Mossman D., Evans T. J., Holliday E. G., Tooney P. A., et al. (2014) Combined analysis of exon splicing and genome wide polymorphism data predict schizophrenia risk loci. J Psychiatr Res 52: 44-49

Onore C., Careaga M. and Ashwood P. (2012) The role of immune dysfunction in the pathophysiology of autism. Brain Behav Immun 26: 383-392

Pan X., Durrett R. E., Zhu H., Tanaka Y., Li Y., et al. (2013) Two methods for full-length RNA sequencing for low quantities of cells and single cells. Proc Natl Acad Sci U S A 110: 594-599

Peleg S., Sananbenesi F., Zovoilis A., Burkhardt S., Bahari-Javan S., et al. (2010) Altered histone acetylation is associated with age-dependent memory impairment in mice. Science 328: 753-756

Persico A. M. and Napolioni V. (2013) Autism genetics. Behav Brain Res 251: 95-112

Poduri A., Evrony G. D., Cai X. and Walsh C. A. (2013) Somatic mutation, genomic variation, and neurological disease. Science 341: 1237758

Pratt J., Winchester C., Dawson N. and Morris B. (2012) Advancing schizophrenia drug discovery: optimizing rodent models to bridge the translational gap. Nat Rev Drug Discov 11: 560-579

Priebe L., Degenhardt F. A., Herms S., Haenisch B., Mattheisen M., et al. (2012) Genome-wide survey implicates the influence of copy number variants (CNVs) in the development of early-onset bipolar disorder. Mol Psychiatry 17: 421-432

Purcell S. M., Moran J. L., Fromer M., Ruderfer D., Solovieff N., et al. (2014) A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506: 185-190

Qin J., Li R., Raes J., Arumugam M., Burgdorf K. S., et al. (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464: 59-65

Quinlan A. R. and Hall I. M. (2012) Characterizing complex structural variation in germline and somatic genomes. Trends Genet 28: 43-53

Rabbani B., Tekin M. and Mahdieh N. (2014) The promise of whole-exome sequencing in medical genetics. J Hum Genet 59: 5-15

Raj T., Ryan K. J., Replogle J. M., Chibnik L. B., Rosenkrantz L., et al. (2014) CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer's disease susceptibility. Hum Mol Genet 23: 2729-2736

Ramos-Quiroga J. A., Sanchez-Mora C., Casas M., Garcia-Martinez I., Bosch R., et al. (2014) Genome-wide copy number variation analysis in adult attention-deficit and hyperactivity disorder. J Psychiatr Res 49: 60-67

Redon R., Ishikawa S., Fitch K. R., Feuk L., Perry G. H., et al. (2006) Global variation in copy number in the human genome. Nature 444: 444-454

Rees E., Kirov G., Sanders A., Walters J. T., Chambert K. D., et al. (2014) Evidence that duplications of 22q11.2 protect against schizophrenia. Mol Psychiatry 19: 37-40

Reyes A., Semenkovich N. P., Whiteson K., Rohwer F. and Gordon J. I. (2012) Going viral: next-generation sequencing applied to phage populations in the human gut. Nat Rev Microbiol 10: 607-617

Ribeiro F. M., Camargos E. R., Souza L. C. and Teixeira A. L. (2013) Animal models of neurodegenerative diseases. Rev Bras Psiquiatr 35 Suppl 2: S82-91

Ripke S., O'Dushlaine C., Chambert K., Moran J. L., Kahler A. K., et al. (2013) Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat Genet 45: 1150-1159


Rousseaux M. W., Marcogliese P. C., Qu D., Hewitt S. J., Seang S., et al. (2012) Progressive dopaminergic cell loss with unilateral-to-bilateral progression in a genetic model of Parkinson disease. Proc Natl Acad Sci U S A 109: 15918-15923

Ryan S. D., Dolatabadi N., Chan S. F., Zhang X., Akhtar M. W., et al. (2013) Isogenic human iPSC Parkinson's model shows nitrosative stress-induced


dysfunction in MEF2-PGC1alpha transcription. Cell 155: 1351-1364

Rye P. D., Booij B. B., Grave G., Lindahl T., Kristiansen L., et al. (2011) A novel blood test for the early detection of Alzheimer's disease. J Alzheimers Dis 23: 121-129

Sakai Y., Shaw C. A., Dawson B. C., Dugas D. V., Al-Mohtaseb Z., et al. (2011) Protein interactome reveals converging molecular pathways among autism disorders. Sci Transl Med 3: 86ra49

Sanders S. J., Ercan-Sencicek A. G., Hus V., Luo R., Murtha M. T., et al. (2011) Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70: 863-885


Savva G. M., Stephan B. C. and Alzheimer's Society Vascular Dementia Systematic Review G. (2010) Epidemiological studies of the effect of stroke on incident dementia: a systematic review. Stroke 41: e41-46

Schaaf C. P. and Zoghbi H. Y. (2011) Solving the autism puzzle a few pieces at a time. Neuron 70: 806-808

Schizophrenia Working Group of the Psychiatric Genomics C. (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511: 421-427

Schloissnig S., Arumugam M., Sunagawa S., Mitreva M., Tap J., et al. (2013) Genomic variation landscape of the human gut microbiome. Nature 493: 45-50

Scholz S. W., Mhyre T., Ressom H., Shah S. and Federoff H. J. (2012) Genomics and bioinformatics of Parkinson's disease. Cold Spring Harb Perspect Med 2: a009449

Schreiber M., Dorschner M. and Tsuang D. (2013) Next-generation sequencing in schizophrenia and other neuropsychiatric disorders. Am J Med Genet B Neuropsychiatr Genet 162B: 671-678

Screen M., Jonson P. H., Raheem O., Palmio J., Laaksonen R., et al. (2014) Abnormal splicing of NEDD4 in myotonic dystrophy type 2: possible link to statin adverse reactions. Am J Pathol 184: 2322-2332

Sekiyama K., Takamatsu Y., Waragai M. and Hashimoto M. (2014) Role of genomics in translational research for Parkinson's disease. Biochem Biophys Res Commun 452: 226-235

Selkoe D. J. (2012) Preventing Alzheimer's disease. Science 337: 1488-1492

Sharma M., Kruger R. and Gasser T. (2014) From genome-wide association studies to next-generation sequencing: lessons from the past and planning for the future. JAMA Neurol 71: 5-6

Shen J., Bronson R. T., Chen D. F., Xia W., Selkoe D. J., et al. (1997) Skeletal and CNS defects in Presenilin-1-deficient mice. Cell 89: 629-639

Sherva R., Tripodis Y., Bennett D. A., Chibnik L. B., Crane P. K., et al. (2014) Genome-wide association study of the rate of cognitive decline in Alzheimer's disease. Alzheimers Dement 10: 45-52

Sibley C. R., Seow Y., Curtis H., Weinberg M. S. and Wood M. J. (2012) Silencing of Parkinson's disease-associated genes with artificial mirtron mimics of miR-1224. Nucleic Acids Res 40: 9863-9875

Siderowf A., Xie S. X., Hurtig H., Weintraub D., Duda J., et al. (2010) CSF amyloid {beta} 1-42 predicts cognitive decline in Parkinson disease. Neurology 75: 1055-1061

Singh S., Kumar A., Agarwal S., Phadke S. R. and Jaiswal Y. (2014) Genetic insight of schizophrenia: past and future perspectives. Gene 535: 97-100

Siniscalco D., Bradstreet J. J., Sych N. and Antonucci N. (2014) Mesenchymal stem cells in treating autism: Novel insights. World J Stem Cells 6: 173-178

Sinkus M. L., Adams C. E., Logel J., Freedman R. and Leonard S. (2013) Expression of immune genes on chromosome 6p21.3-22.1 in schizophrenia. Brain Behav Immun 32: 51-62

Smith K. R. and Matson J. L. (2010) Social skills: differences among adults with intellectual disabilities, co-morbid autism spectrum disorders and epilepsy. Res Dev Disabil 31: 1366-1372

Stefansson H., Meyer-Lindenberg A., Steinberg S., Magnusdottir B., Morgen K., et al. (2014) CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 505: 361-366

Stilling R. M., Dinan T. G. and Cryan J. F. (2014) Microbial genes, brain & behaviour - epigenetic regulation of the gut-brain axis. Genes Brain Behav 13: 69-86

Stoll G., Pietilainen O. P., Linder B., Suvisaari J., Brosi C., et al. (2013) Deletion of TOP3beta, a component of FMRP-containing mRNPs, contributes to neurodevelopmental disorders. Nat Neurosci 16: 1228-1237

Sun Y., Christensen J. and Olsen J. (2013) Childhood epilepsy and maternal antibodies to microbial and tissue antigens during pregnancy. Epilepsy Res 107: 61-74

Szatkiewicz J. P., O'Dushlaine C., Chen G., Chambert K., Moran J. L., et al. (2014) Copy number variation in schizophrenia in Sweden. Mol Psychiatry 19: 762-773

Szatmari P. (1999) Heterogeneity and the genetics of autism. J Psychiatry Neurosci 24: 159-165

Todarello G., Feng N., Kolachana B. S., Li C., Vakkalanka R., et al. (2014) Incomplete penetrance of NRXN1 deletions in families with schizophrenia. Schizophr Res 155: 1-7

Torrey E. F. and Yolken R. H. (2003) Toxoplasma gondii and schizophrenia. Emerg Infect Dis 9: 1375-1380

Trinh J. and Farrer M. (2013) Advances in the genetics of Parkinson disease. Nat Rev Neurol 9: 445-454

Tucker T., Zahir F. R., Griffith M., Delaney A., Chai D., et al. (2014) Single exon-resolution targeted chromosomal microarray analysis of known and candidate intellectual disability genes. Eur J Hum Genet 22: 792-800

Vidaki A., Daniel B. and Court D. S. (2013) Forensic DNA methylation profiling--potential opportunities and challenges. Forensic Sci Int Genet 7: 499-507

Visscher P. M., Brown M. A., McCarthy M. I. and Yang J. (2012) Five years of GWAS discovery. Am J Hum Genet 90: 7-24

Wang S. C., Oelze B. and Schumacher A. (2008) Age-specific epigenetic drift in late-onset Alzheimer's disease. PLoS One 3: e2698

Watson K. K. and Platt M. L. (2012) Of mice and monkeys: using non-human primate models to bridge mouse- and human-based investigations of autism spectrum disorders. J Neurodev Disord 4: 21

Weaver W. M., Tseng P., Kunze A., Masaeli M., Chung A. J., et al. (2014) Advances in high-throughput single-cell microtechnologies. Curr Opin Biotechnol 25: 114-123

Willsey A. J., Sanders S. J., Li M., Dong S., Tebbenkamp A. T., et al. (2013) Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155: 997-1007



Wright J. (2014) Genetics: Unravelling complexity. Nature 508: S6-7

Yoon K. J., Nguyen H. N., Ursini G., Zhang F., Kim N. S., et al. (2014) Modeling a genetic risk for schizophrenia in iPSCs and mice reveals neural stem cell deficits associated with adherens junctions and polarity. Cell Stem Cell 15: 79-91

Young G. T., Gutteridge A., Fox H. D., Wilbrey A. L., Cao L., et al. (2014) Characterizing Human Stem Cell-derived Sensory Neurons at the Single-cell Level Reveals Their Ion Channel Expression and Utility in Pain Research. Mol Ther 22: 1530-1543


Yu T. W., Chahrour M. H., Coulter M. E., Jiralerspong S., Okamura-Ikeda K., et al. (2013) Using whole-exome sequencing to identify inherited causes of autism. Neuron 77: 259-273

Zarowiecki M. (2012) Metagenomics with guts. Nat Rev Microbiol 10: 674

Zhang B., Gaiteri C., Bodea L. G., Wang Z., McElwee J., et al. (2013) Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153: 707-720

Zhang D., Cheng L., Qian Y., Alliey-Rodriguez N., Kelsoe J. R., et al. (2009) Singleton deletions throughout the genome increase risk of bipolar disorder. Mol Psychiatry 14: 376-380

Zhang K., Schrag M., Crofton A., Trivedi R., Vinters H., et al. (2012) Targeted proteomics for quantification of histone acetylation in Alzheimer's disease. Proteomics 12: 1261-1268

Zhang Y., Schulz V. P., Reed B. D., Wang Z., Pan X., et al. (2013) Functional genomic screen of human stem cell differentiation reveals pathways involved in neurodevelopment and neurodegeneration. Proc Natl Acad Sci U S A 110: 12361-12366

Zhou H., Huang C., Tong J., Hong W. C., Liu Y. J., et al. (2011) Temporal expression of mutant LRRK2 in adult rats impairs dopamine reuptake. Int J Biol Sci 7: 753-761

Zhou W., Bercury K., Cummiskey J., Luong N., Lebin J., et al. (2011) Phenylbutyrate up-regulates the DJ-1 protein and protects neurons in cell culture and in animal models of Parkinson disease. J Biol Chem 286: 14941-14951

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